Nothing
tests/testthat/test-predict.R
In multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data
smk_net <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
# Only test gradients, no sampling
smk_fit_RE <- suppressWarnings(nma(smk_net,
trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5),
iter = 10))
test_that("baseline argument", {
m <- "should be specified using distr"
expect_error(predict(smk_fit_RE, baseline = 1), m)
expect_error(predict(smk_fit_RE, baseline = list("a")), m)
expect_error(predict(smk_fit_RE, baseline = NA), m)
expect_error(predict(smk_fit_RE, baseline = "a"), "`baseline` must match the name of an IPD or AgD \\(arm-based\\) study in the network")
})
test_that("trt_ref argument", {
m <- "does not match a treatment in the network"
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = "a"), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = 1), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = list("a")), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = NA), m)
})
test_that("probs argument", {
m <- "numeric vector of probabilities"
expect_error(predict(smk_fit_RE, probs = "a"), m)
expect_error(predict(smk_fit_RE, probs = -1), m)
expect_error(predict(smk_fit_RE, probs = 1.5), m)
expect_error(predict(smk_fit_RE, probs = Inf), m)
expect_error(predict(smk_fit_RE, probs = list()), m)
expect_error(predict(smk_fit_RE, probs = NA), m)
expect_error(predict(smk_fit_RE, probs = NULL), m)
})
test_that("summary argument", {
m <- "should be TRUE or FALSE"
expect_error(predict(smk_fit_RE, summary = "a"), m)
expect_error(predict(smk_fit_RE, summary = 1), m)
expect_error(predict(smk_fit_RE, summary = list()), m)
expect_error(predict(smk_fit_RE, summary = NA), m)
expect_error(predict(smk_fit_RE, summary = NULL), m)
})
test_that("newdata argument", {
m <- "not a data frame"
expect_error(predict(smk_fit_RE, newdata = "a"), m)
expect_error(predict(smk_fit_RE, newdata = 1), m)
expect_error(predict(smk_fit_RE, newdata = list()), m)
expect_error(predict(smk_fit_RE, newdata = NA), m)
})
test_that("type argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, type = "a"), m)
expect_error(predict(smk_fit_RE, type = "lin"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, type = 1), m2)
expect_error(predict(smk_fit_RE, type = list("a")), m2)
expect_error(predict(smk_fit_RE, type = NA), m2)
})
test_that("level argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, level = "a"), m)
expect_error(predict(smk_fit_RE, level = "agg"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, level = 1), m2)
expect_error(predict(smk_fit_RE, level = list("a")), m2)
expect_error(predict(smk_fit_RE, level = NA), m2)
expect_error(predict(smk_fit_RE, level = "individual"),
"Cannot produce individual predictions without a regression model.")
})
test_that("baseline_type argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, baseline_type = "a"), m)
expect_error(predict(smk_fit_RE, baseline_type = "lin"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, baseline_type = 1), m2)
expect_error(predict(smk_fit_RE, baseline_type = list("a")), m2)
expect_error(predict(smk_fit_RE, baseline_type = NA), m2)
})
test_that("baseline_level argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, baseline_level = "a"), m)
expect_error(predict(smk_fit_RE, baseline_level = "agg"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, baseline_level = 1), m2)
expect_error(predict(smk_fit_RE, baseline_level = list("a")), m2)
expect_error(predict(smk_fit_RE, baseline_level = NA), m2)
})
skip_on_cran() # Reduce CRAN check time
test_that(".study, .trt columns are correct", {
pred1 <- tibble::as_tibble(predict(smk_fit_RE))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(smk_fit_RE, baseline = distr(qnorm, 0, 1)))
expect_identical(paste0("pred[", pred2$.trt, "]"),
pred2$parameter)
})
pso_net <- set_ipd(plaque_psoriasis_ipd[complete.cases(plaque_psoriasis_ipd), ],
studyc, trtc,
r = pasi75)
# Only small number of samples to test
pso_fit <- suppressWarnings(nma(pso_net,
trt_effects = "fixed",
regression = ~(durnpso + prevsys + bsa + weight + psa)*.trt,
prior_intercept = normal(scale = 10),
prior_trt = normal(scale = 10),
prior_reg = normal(scale = 10),
init_r = 0.1,
iter = 10))
pso_new <- data.frame(durnpso = 10, prevsys = TRUE, bsa = 20, weight = 75, psa = FALSE, study = c("One", "Two"))
test_that("baseline and newdata for regression models", {
m <- "Specify both `newdata` and `baseline`, or neither"
expect_error(predict(pso_fit, newdata = pso_new), m)
expect_error(predict(pso_fit, baseline = distr(qnorm, 1, 1)), m)
})
test_that("baseline argument", {
m <- "should be a single distr\\(\\) specification or character string naming a study in the network, a list of such specifications, or NULL"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = 1), m)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = NA), m)
m2 <- "or a list of length 2"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(distr(qnorm, 1, 1),
distr(qnorm, 2, 1),
distr(qnorm, 3, 1))), m2)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(One = distr(qnorm, 1, 1),
Two = distr(qnorm, 2, 1),
Three = distr(qnorm, 3, 1))), m2)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(One = distr(qnorm, 1, 1),
Three = distr(qnorm, 3, 1))),
"must match all study names")
m3 <- "must match the name of an IPD or AgD \\(arm-based\\) study in the network"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = "a"), m3)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list("a")), m3)
})
test_that("newdata validation", {
expect_error(predict(pso_fit, newdata = pso_new[-1], study = study, baseline = distr(qnorm, 1, 1)),
'Regression variable "durnpso" not found in `newdata`')
expect_error(predict(pso_fit, newdata = pso_new[-(1:2)], study = study, baseline = distr(qnorm, 1, 1)),
'Regression variables "durnpso" and "prevsys" not found in `newdata`')
make_bad <- function(x, vars = "durnpso") {
bad <- pso_new
bad[vars] <- x
return(bad)
}
expect_error(predict(pso_fit, newdata = make_bad(NA), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(NaN), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(Inf), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(NA, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
expect_error(predict(pso_fit, newdata = make_bad(NaN, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
expect_error(predict(pso_fit, newdata = make_bad(Inf, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
})
test_that(".study, .trt columns are correct", {
pred1 <- tibble::as_tibble(predict(pso_fit))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(pso_fit, newdata = pso_new, study = study, baseline = distr(qnorm, 1, 1)))
expect_identical(paste0("pred[", pred2$.study, ": ", pred2$.trt, "]"),
pred2$parameter)
})
hta_net <- set_agd_arm(hta_psoriasis,
study = paste(studyc, year),
trt = trtc,
r = multi(r0 = sample_size - rowSums(cbind(PASI50, PASI75, PASI90), na.rm = TRUE),
PASI50, PASI75, PASI90,
inclusive = FALSE,
type = "ordered"))
# Only small number of samples to test
hta_fit_FE <- suppressWarnings(nma(hta_net,
trt_effects = "fixed",
link = "probit",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 10),
prior_aux = flat(),
iter = 10))
test_that(".study, .trt, .category columns are correct", {
pred1 <- tibble::as_tibble(predict(hta_fit_FE))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, ", ", pred1$.category, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(hta_fit_FE, baseline = distr(qnorm, 0, 1)))
expect_identical(paste0("pred[", pred2$.trt, ", ", pred2$.category, "]"),
pred2$parameter)
})
ndmm_net <- combine_network(
set_ipd(dplyr::slice_sample(ndmm_ipd, n = 10, by = c("study", "trt")),
study, trt,
Surv = Surv(eventtime/7, status),
trt_class = trt != "Pbo"),
set_agd_surv(dplyr::slice_sample(ndmm_agd, n= 10, by = c("study", "trt")),
study, trt,
Surv = Surv(eventtime/7, status),
covariates = ndmm_agd_covs,
trt_class = trt != "Pbo")
) %>%
add_integration(age = distr(qnorm, age_mean, age_sd),
n_int = 5)
# Only small number of samples to test
ndmm_fit_weib <- suppressWarnings(nma(ndmm_net,
likelihood = "weibull-aft",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = half_normal(10),
iter = 10))
ndmm_fit_exp <- suppressWarnings(nma(ndmm_net,
likelihood = "exponential",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
iter = 10))
ndmm_fit_gengamma <- suppressWarnings(nma(ndmm_net,
likelihood = "gengamma",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = list(sigma = half_normal(5), k = half_normal(5)),
iter = 10))
ndmm_fit_mspline <- suppressWarnings(nma(ndmm_net,
likelihood = "mspline",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = half_normal(1),
iter = 10))
ndmm_preddat <- dplyr::bind_rows(
dplyr::transmute(ndmm_net$ipd, .study, .trt, .time = .Surv[, "time"]),
tidyr::unnest(ndmm_net$agd_arm, cols = ".Surv") %>% dplyr::transmute(.study, .trt, .time = .Surv[, "time"]),
)
test_that(".study, .trt, .time columns are correct (weibull, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# expect_warning(
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "mean")),
# "Evaluating M-spline at times beyond the boundary knots")
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "median")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "quantile", quantiles = qs)),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that("Survival predictions for new studies require correct args", {
m <- "Specify both `baseline` and `aux`, or neither"
expect_error(predict(ndmm_fit_weib, type = "survival",
times = 0:5,
baseline = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib, type = "survival",
times = 0:5,
aux = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib, type = "survival",
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
"`times` must be specified")
})
test_that(".study, .trt, .time columns are correct (weibull, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "mean",
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "median",
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "mean",
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "median",
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "mean",
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "median",
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that("errors for aux lists", {
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 0.1))),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1))),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.1)),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
})
test_that(".study, .trt, .time columns are correct (mspline, no regression, new data)", {
expect_error(predict(ndmm_fit_mspline, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
})
ndmm_fit_weib_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "weibull-aft",
regression = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = half_normal(10),
iter = 10,
seed = 42))
ndmm_fit_exp_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "exponential",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
iter = 10))
ndmm_fit_gengamma_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "gengamma",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = list(sigma = half_normal(5), k = half_normal(5)),
init_r = 0.1,
iter = 10))
ndmm_fit_mspline_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "mspline",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = half_normal(1),
iter = 10))
test_that("Survival predictions for new studies require correct args", {
m <- "Specify all of `newdata`, `baseline`, and `aux`, or none"
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
baseline = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
times = 0:5,
aux = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
m)
})
test_that(".study, .trt, .time columns are correct (weibull, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# expect_warning(
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean", level = "individual")),
# "Evaluating M-spline at times beyond the boundary knots")
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median", level = "individual")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs,
level = "individual")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs)),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
expect_error(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.1b <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = "Attal2012",
aux = "Attal2012"))
expect_equivalent(pred1.1b[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1b$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "rmst", time = 3,
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
expect_error(predict(ndmm_fit_mspline_reg, type = "survival", time = time, level = "individual",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.1b <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = "Attal2012",
aux = "Attal2012"))
expect_equivalent(pred1.1b[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1b$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that("errors for aux lists (regression, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time, study = study,
baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 0.1))),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1))),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.1)),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
})
test_that("aux argument", {
newdata <- tibble::tibble(study = c("a", "b"), age = 4, time = 1)
# Single aux parameter, no regression
m <- "`aux` must be specified using distr\\(\\), or the name of an IPD or AgD \\(arm-based\\) study in the network"
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"`aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
# Single aux parameter, regression
m2 <- "`aux` must be a single distr\\(\\) specification or study name, or a list of length 2 \\(number of `newdata` studies\\)"
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(a = distr(qnorm, 1, 1),
c = distr(qnorm, 1, 1))),
"must match all study names from `newdata`")
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(a = distr(qnorm, 1, 1),
b = "bad")),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
# Multiple aux parameters, no regression
m3 <- "`aux` must be a named list of distr\\(\\) specifications for sigma and k, or a study name"
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"`aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1))), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1))), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1),
sigma = "bad")), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1),
bad = distr(qlnorm, 0, 1))), m3)
# Multiple aux parameters, regression
m4 <- "`aux` must be a single named list of distr\\(\\) specifications for sigma and k, a study name, or a list of length 2 \\(number of `newdata` studies\\) of such lists"
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a list of distr\\(\\) distributions")
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1),
bad = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
c = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = "bad")), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1),
k = "bad")
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = "bad")
), "All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a list of distr\\(\\) distributions")
})
Try the multinma package in your browser
Any scripts or data that you put into this service are public.
multinma documentation built on June 22, 2024, 9:10 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
smk_net <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
# Only test gradients, no sampling
smk_fit_RE <- suppressWarnings(nma(smk_net,
trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5),
iter = 10))
test_that("baseline argument", {
m <- "should be specified using distr"
expect_error(predict(smk_fit_RE, baseline = 1), m)
expect_error(predict(smk_fit_RE, baseline = list("a")), m)
expect_error(predict(smk_fit_RE, baseline = NA), m)
expect_error(predict(smk_fit_RE, baseline = "a"), "`baseline` must match the name of an IPD or AgD \\(arm-based\\) study in the network")
})
test_that("trt_ref argument", {
m <- "does not match a treatment in the network"
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = "a"), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = 1), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = list("a")), m)
expect_error(predict(smk_fit_RE, baseline = distr(qnorm, mean = -1, sd = 0.01), trt_ref = NA), m)
})
test_that("probs argument", {
m <- "numeric vector of probabilities"
expect_error(predict(smk_fit_RE, probs = "a"), m)
expect_error(predict(smk_fit_RE, probs = -1), m)
expect_error(predict(smk_fit_RE, probs = 1.5), m)
expect_error(predict(smk_fit_RE, probs = Inf), m)
expect_error(predict(smk_fit_RE, probs = list()), m)
expect_error(predict(smk_fit_RE, probs = NA), m)
expect_error(predict(smk_fit_RE, probs = NULL), m)
})
test_that("summary argument", {
m <- "should be TRUE or FALSE"
expect_error(predict(smk_fit_RE, summary = "a"), m)
expect_error(predict(smk_fit_RE, summary = 1), m)
expect_error(predict(smk_fit_RE, summary = list()), m)
expect_error(predict(smk_fit_RE, summary = NA), m)
expect_error(predict(smk_fit_RE, summary = NULL), m)
})
test_that("newdata argument", {
m <- "not a data frame"
expect_error(predict(smk_fit_RE, newdata = "a"), m)
expect_error(predict(smk_fit_RE, newdata = 1), m)
expect_error(predict(smk_fit_RE, newdata = list()), m)
expect_error(predict(smk_fit_RE, newdata = NA), m)
})
test_that("type argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, type = "a"), m)
expect_error(predict(smk_fit_RE, type = "lin"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, type = 1), m2)
expect_error(predict(smk_fit_RE, type = list("a")), m2)
expect_error(predict(smk_fit_RE, type = NA), m2)
})
test_that("level argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, level = "a"), m)
expect_error(predict(smk_fit_RE, level = "agg"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, level = 1), m2)
expect_error(predict(smk_fit_RE, level = list("a")), m2)
expect_error(predict(smk_fit_RE, level = NA), m2)
expect_error(predict(smk_fit_RE, level = "individual"),
"Cannot produce individual predictions without a regression model.")
})
test_that("baseline_type argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, baseline_type = "a"), m)
expect_error(predict(smk_fit_RE, baseline_type = "lin"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, baseline_type = 1), m2)
expect_error(predict(smk_fit_RE, baseline_type = list("a")), m2)
expect_error(predict(smk_fit_RE, baseline_type = NA), m2)
})
test_that("baseline_level argument", {
m <- "must be one of"
expect_error(predict(smk_fit_RE, baseline_level = "a"), m)
expect_error(predict(smk_fit_RE, baseline_level = "agg"), m)
m2 <- "must be a character vector"
expect_error(predict(smk_fit_RE, baseline_level = 1), m2)
expect_error(predict(smk_fit_RE, baseline_level = list("a")), m2)
expect_error(predict(smk_fit_RE, baseline_level = NA), m2)
})
skip_on_cran() # Reduce CRAN check time
test_that(".study, .trt columns are correct", {
pred1 <- tibble::as_tibble(predict(smk_fit_RE))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(smk_fit_RE, baseline = distr(qnorm, 0, 1)))
expect_identical(paste0("pred[", pred2$.trt, "]"),
pred2$parameter)
})
pso_net <- set_ipd(plaque_psoriasis_ipd[complete.cases(plaque_psoriasis_ipd), ],
studyc, trtc,
r = pasi75)
# Only small number of samples to test
pso_fit <- suppressWarnings(nma(pso_net,
trt_effects = "fixed",
regression = ~(durnpso + prevsys + bsa + weight + psa)*.trt,
prior_intercept = normal(scale = 10),
prior_trt = normal(scale = 10),
prior_reg = normal(scale = 10),
init_r = 0.1,
iter = 10))
pso_new <- data.frame(durnpso = 10, prevsys = TRUE, bsa = 20, weight = 75, psa = FALSE, study = c("One", "Two"))
test_that("baseline and newdata for regression models", {
m <- "Specify both `newdata` and `baseline`, or neither"
expect_error(predict(pso_fit, newdata = pso_new), m)
expect_error(predict(pso_fit, baseline = distr(qnorm, 1, 1)), m)
})
test_that("baseline argument", {
m <- "should be a single distr\\(\\) specification or character string naming a study in the network, a list of such specifications, or NULL"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = 1), m)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = NA), m)
m2 <- "or a list of length 2"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(distr(qnorm, 1, 1),
distr(qnorm, 2, 1),
distr(qnorm, 3, 1))), m2)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(One = distr(qnorm, 1, 1),
Two = distr(qnorm, 2, 1),
Three = distr(qnorm, 3, 1))), m2)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list(One = distr(qnorm, 1, 1),
Three = distr(qnorm, 3, 1))),
"must match all study names")
m3 <- "must match the name of an IPD or AgD \\(arm-based\\) study in the network"
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = "a"), m3)
expect_error(predict(pso_fit, study = study, newdata = pso_new, baseline = list("a")), m3)
})
test_that("newdata validation", {
expect_error(predict(pso_fit, newdata = pso_new[-1], study = study, baseline = distr(qnorm, 1, 1)),
'Regression variable "durnpso" not found in `newdata`')
expect_error(predict(pso_fit, newdata = pso_new[-(1:2)], study = study, baseline = distr(qnorm, 1, 1)),
'Regression variables "durnpso" and "prevsys" not found in `newdata`')
make_bad <- function(x, vars = "durnpso") {
bad <- pso_new
bad[vars] <- x
return(bad)
}
expect_error(predict(pso_fit, newdata = make_bad(NA), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(NaN), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(Inf), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso")
expect_error(predict(pso_fit, newdata = make_bad(NA, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
expect_error(predict(pso_fit, newdata = make_bad(NaN, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
expect_error(predict(pso_fit, newdata = make_bad(Inf, c("durnpso", "psa")), study = study, baseline = distr(qnorm, 1, 1)),
"missing or infinite values in `newdata`: durnpso, psa")
})
test_that(".study, .trt columns are correct", {
pred1 <- tibble::as_tibble(predict(pso_fit))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(pso_fit, newdata = pso_new, study = study, baseline = distr(qnorm, 1, 1)))
expect_identical(paste0("pred[", pred2$.study, ": ", pred2$.trt, "]"),
pred2$parameter)
})
hta_net <- set_agd_arm(hta_psoriasis,
study = paste(studyc, year),
trt = trtc,
r = multi(r0 = sample_size - rowSums(cbind(PASI50, PASI75, PASI90), na.rm = TRUE),
PASI50, PASI75, PASI90,
inclusive = FALSE,
type = "ordered"))
# Only small number of samples to test
hta_fit_FE <- suppressWarnings(nma(hta_net,
trt_effects = "fixed",
link = "probit",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 10),
prior_aux = flat(),
iter = 10))
test_that(".study, .trt, .category columns are correct", {
pred1 <- tibble::as_tibble(predict(hta_fit_FE))
expect_identical(paste0("pred[", pred1$.study, ": ", pred1$.trt, ", ", pred1$.category, "]"),
pred1$parameter)
pred2 <- tibble::as_tibble(predict(hta_fit_FE, baseline = distr(qnorm, 0, 1)))
expect_identical(paste0("pred[", pred2$.trt, ", ", pred2$.category, "]"),
pred2$parameter)
})
ndmm_net <- combine_network(
set_ipd(dplyr::slice_sample(ndmm_ipd, n = 10, by = c("study", "trt")),
study, trt,
Surv = Surv(eventtime/7, status),
trt_class = trt != "Pbo"),
set_agd_surv(dplyr::slice_sample(ndmm_agd, n= 10, by = c("study", "trt")),
study, trt,
Surv = Surv(eventtime/7, status),
covariates = ndmm_agd_covs,
trt_class = trt != "Pbo")
) %>%
add_integration(age = distr(qnorm, age_mean, age_sd),
n_int = 5)
# Only small number of samples to test
ndmm_fit_weib <- suppressWarnings(nma(ndmm_net,
likelihood = "weibull-aft",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = half_normal(10),
iter = 10))
ndmm_fit_exp <- suppressWarnings(nma(ndmm_net,
likelihood = "exponential",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
iter = 10))
ndmm_fit_gengamma <- suppressWarnings(nma(ndmm_net,
likelihood = "gengamma",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = list(sigma = half_normal(5), k = half_normal(5)),
iter = 10))
ndmm_fit_mspline <- suppressWarnings(nma(ndmm_net,
likelihood = "mspline",
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_aux = half_normal(1),
iter = 10))
ndmm_preddat <- dplyr::bind_rows(
dplyr::transmute(ndmm_net$ipd, .study, .trt, .time = .Surv[, "time"]),
tidyr::unnest(ndmm_net$agd_arm, cols = ".Surv") %>% dplyr::transmute(.study, .trt, .time = .Surv[, "time"]),
)
test_that(".study, .trt, .time columns are correct (weibull, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, no regression, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# expect_warning(
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "mean")),
# "Evaluating M-spline at times beyond the boundary knots")
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "median")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline, type = "quantile", quantiles = qs)),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that("Survival predictions for new studies require correct args", {
m <- "Specify both `baseline` and `aux`, or neither"
expect_error(predict(ndmm_fit_weib, type = "survival",
times = 0:5,
baseline = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib, type = "survival",
times = 0:5,
aux = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib, type = "survival",
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
"`times` must be specified")
})
test_that(".study, .trt, .time columns are correct (weibull, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "mean",
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "median",
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "mean",
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "median",
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, no regression, new data)", {
time <- 0:5
# Prediction format new times
preddat1 <- tidyr::expand_grid(.study = "New 1",
.trt = unique(ndmm_preddat$.trt),
.time = time) %>%
dplyr::group_by(.trt) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup()
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "hazard",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "cumhaz",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".trt", ".time")],
preddat1[, c(".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tibble::tibble(.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "mean",
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, ".trt"],
# preddat3)
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "median",
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, ".trt"],
preddat3)
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "rmst",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".trt", ".time")],
dplyr::mutate(preddat3, .time = 3))
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "link",
time = 3,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, ".trt"],
preddat3)
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- tidyr::expand_grid(.trt = unique(ndmm_preddat$.trt),
.quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma, type = "quantile", quantiles = qs,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that("errors for aux lists", {
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 0.1))),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1))),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.1)),
"`aux` must be a named list of distr\\(\\) specifications for sigma and k")
})
test_that(".study, .trt, .time columns are correct (mspline, no regression, new data)", {
expect_error(predict(ndmm_fit_mspline, type = "survival",
times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
})
ndmm_fit_weib_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "weibull-aft",
regression = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = half_normal(10),
iter = 10,
seed = 42))
ndmm_fit_exp_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "exponential",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
iter = 10))
ndmm_fit_gengamma_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "gengamma",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = list(sigma = half_normal(5), k = half_normal(5)),
init_r = 0.1,
iter = 10))
ndmm_fit_mspline_reg <- suppressWarnings(nma(ndmm_net,
likelihood = "mspline",
reg = ~age*.trt,
prior_intercept = normal(0, 100),
prior_trt = normal(0, 10),
prior_reg = normal(0, 10),
prior_aux = half_normal(1),
iter = 10))
test_that("Survival predictions for new studies require correct args", {
m <- "Specify all of `newdata`, `baseline`, and `aux`, or none"
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
baseline = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
times = 0:5,
aux = distr(qnorm, 0, 1)),
m)
expect_error(predict(ndmm_fit_weib_reg, type = "survival",
baseline = distr(qnorm, 0, 1),
aux = distr(qnorm, 0, 1)),
m)
})
test_that(".study, .trt, .time columns are correct (weibull, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean", level = "individual"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median", level = "individual"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", quantiles = qs,
level = "individual"))
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, individual, network data)", {
# Prediction format for level = "individual", observed times
preddat1 <- dplyr::filter(ndmm_preddat, .study %in% unique(ndmm_ipd$study)) %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt))
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Single summaries, also at individual times
# expect_warning(
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean", level = "individual")),
# "Evaluating M-spline at times beyond the boundary knots")
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median", level = "individual")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", level = "individual"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat1[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link", level = "individual"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat1[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat1, tibble::tibble(.quantile = qs))
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs,
level = "individual")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".trt", ".quantile")],
preddat4[, c(".trt", ".quantile")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median"))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, aggregate, network data)", {
# Prediction format for observed times
preddat1 <- ndmm_preddat %>%
dplyr::group_by(.study) %>%
dplyr::mutate(id = 1:dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::select(-.trt) %>%
dplyr::cross_join(dplyr::distinct(ndmm_preddat, .trt)) %>%
dplyr::arrange(.study, .trt)
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for provided times
times <- 0:5
preddat2 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt) %>%
dplyr::mutate(.time = list(times), id = list(seq_along(times))) %>%
tidyr::unnest(cols = c(".time", "id"))
pred2.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", times = times))
expect_equivalent(pred2.1[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.1$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", times = times))
expect_equivalent(pred2.2[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.2$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", times = times))
expect_equivalent(pred2.3[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.3$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt)
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "rmst"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::distinct(ndmm_preddat, .study, .trt) %>%
tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs)),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, aggregate, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
expect_error(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.1b <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = time,
study = study,
newdata = newdata,
baseline = "Attal2012",
aux = "Attal2012"))
expect_equivalent(pred1.1b[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1b$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
pred1.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.4[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.4$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.5 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.5[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.5$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.6 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.6[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.6$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Prediction format for single summaries
preddat3 <- tidyr::expand_grid(.study = unique(factor(newdata$study)),
.trt = unique(ndmm_preddat$.trt))
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "rmst", time = 3,
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.3[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.3$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- preddat3 %>% tidyr::expand(.study, .trt, .quantile = qs)
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (weibull, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = distr(qlnorm, 0, 0.01)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_weib_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (exponential, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1)))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_exp_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1)))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (gengamma, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = list(sigma = distr(qlnorm, 0, 0.1),
# k = distr(qlnorm, 0, 0.1))))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_gengamma_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1),
k = distr(qlnorm, 0, 0.1))))
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that(".study, .trt, .time columns are correct (mspline, regression, individual, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm, age = rlnorm(length(tm), log(4.5), 0.25))
expect_error(predict(ndmm_fit_mspline_reg, type = "survival", time = time, level = "individual",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.01)),
'Producing predictions with external `aux` spline coefficients is not currently supported for "mspline" models.')
# Prediction format new times
preddat1 <- dplyr::mutate(newdata, .study = factor(study), .time = tm, id = 1:dplyr::n()) %>%
dplyr::cross_join(dplyr::tibble(.trt = unique(ndmm_preddat$.trt)))
# Times from newdata column
pred1.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.1[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.1b <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = "Attal2012",
aux = "Attal2012"))
expect_equivalent(pred1.1b[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.1b$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.2[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.2$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
pred1.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual",
time = time,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred1.3[, c(".study", ".trt", ".time")],
preddat1[, c(".study", ".trt", ".time")])
expect_identical(pred1.3$parameter,
paste0("pred[", preddat1$.study, ": ", preddat1$.trt, ", ", preddat1$id, "]"))
# Times from global env
# With external times vector, evaluate every time and every treatment for every new individual
preddat2 <- dplyr::mutate(newdata, .study = factor(study)) %>%
dplyr::cross_join(
dplyr::cross_join(dplyr::tibble(.time = tm, id = 1:length(tm)),
dplyr::tibble(.trt = unique(ndmm_preddat$.trt))))
pred2.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "survival", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.4[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.4$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.5 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "hazard", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.5[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.5$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
pred2.6 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "cumhaz", level = "individual",
time = tm,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred2.6[, c(".study", ".trt", ".time")],
preddat2[, c(".study", ".trt", ".time")])
expect_identical(pred2.6$parameter,
paste0("pred[", preddat2$.study, ": ", preddat2$.trt, ", ", preddat2$id, "]"))
# Prediction format for single summaries
preddat3 <- preddat1
# pred3.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
# type = "mean",
# study = study,
# newdata = newdata,
# baseline = distr(qnorm, 0, 1),
# aux = "Attal2012"))
# expect_equivalent(pred3.1[, c(".study", ".trt")],
# preddat3[, c(".study", ".trt")])
# expect_identical(pred3.1$parameter,
# paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
expect_warning(
pred3.2 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "median",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred3.2[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.2$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.3 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "rmst", time = 3,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.3[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.3$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
pred3.4 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, level = "individual",
type = "link",
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012"))
expect_equivalent(pred3.4[, c(".study", ".trt")],
preddat3[, c(".study", ".trt")])
expect_identical(pred3.4$parameter,
paste0("pred[", preddat3$.study, ": ", preddat3$.trt, ", ", preddat3$id, "]"))
# Prediction format for quantiles
qs <- c(0.2, 0.4, 0.6, 0.8)
preddat4 <- dplyr::cross_join(preddat3, dplyr::tibble(.quantile = qs))
expect_warning(
pred4.1 <- tibble::as_tibble(predict(ndmm_fit_mspline_reg, type = "quantile", level = "individual",
quantiles = qs,
study = study,
newdata = newdata,
baseline = distr(qnorm, 0, 1),
aux = "Attal2012")),
"Evaluating M-spline at times beyond the boundary knots")
expect_equivalent(pred4.1[, c(".study", ".trt")],
preddat4[, c(".study", ".trt")])
expect_identical(pred4.1$parameter,
paste0("pred[", preddat4$.study, ": ", preddat4$.trt, ", ", preddat4$id, ", ", preddat4$.quantile, "]"))
})
test_that("errors for aux lists (regression, new data)", {
tm <- 0:5
newdata <- dplyr::tibble(study = "Test", time = tm) %>%
add_integration(age = distr(qlnorm, meanlog = log(4.5), sdlog = 1), n_int = 5)
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time, study = study,
baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 0.1))),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time,
baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 0.1))),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma_reg, type = "survival",
newdata = newdata, times = time,
baseline = distr(qnorm, 0, 1),
aux = distr(qlnorm, 0, 0.1)),
"`aux` must be a single named list of distr\\(\\) specifications for sigma and k")
})
test_that("aux argument", {
newdata <- tibble::tibble(study = c("a", "b"), age = 4, time = 1)
# Single aux parameter, no regression
m <- "`aux` must be specified using distr\\(\\), or the name of an IPD or AgD \\(arm-based\\) study in the network"
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m)
expect_error(predict(ndmm_fit_weib, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"`aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
# Single aux parameter, regression
m2 <- "`aux` must be a single distr\\(\\) specification or study name, or a list of length 2 \\(number of `newdata` studies\\)"
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m2)
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(a = distr(qnorm, 1, 1),
c = distr(qnorm, 1, 1))),
"must match all study names from `newdata`")
expect_error(predict(ndmm_fit_weib_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(a = distr(qnorm, 1, 1),
b = "bad")),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a distr\\(\\) distribution")
# Multiple aux parameters, no regression
m3 <- "`aux` must be a named list of distr\\(\\) specifications for sigma and k, or a study name"
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"`aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a named list of distr\\(\\) specifications for sigma and k")
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1))), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1))), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1),
sigma = "bad")), m3)
expect_error(predict(ndmm_fit_gengamma, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1),
bad = distr(qlnorm, 0, 1))), m3)
# Multiple aux parameters, regression
m4 <- "`aux` must be a single named list of distr\\(\\) specifications for sigma and k, a study name, or a list of length 2 \\(number of `newdata` studies\\) of such lists"
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = 1), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = list("a")), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = NA), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1), aux = "a"),
"All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a list of distr\\(\\) distributions")
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(k = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1),
bad = distr(qlnorm, 0, 1))), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
c = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1))
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(sigma = distr(qlnorm, 0, 1),
k = "bad")), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = list(sigma = distr(qlnorm, 0, 1),
k = "bad")
)), m4)
expect_error(predict(ndmm_fit_gengamma_reg, times = 1, study = study, newdata = newdata, baseline = distr(qnorm, 0, 1),
aux = list(
a = list(sigma = distr(qlnorm, 0, 1),
k = distr(qlnorm, 0, 1)),
b = "bad")
), "All elements of `aux` must match the name of an IPD or AgD \\(arm-based\\) study in the network, or be a list of distr\\(\\) distributions")
})
Try the multinma package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.