tests/testthat/test-data_combine.R
In dmphillippo/multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data
library(multinma)
library(dplyr)
test_that("combine_network error if not passed nma_data objects", {
msg <- "Expecting to combine objects of class `nma_data`, created using set_\\* functions"
expect_error(combine_network(1), msg)
expect_error(combine_network(1, 2), msg)
expect_error(combine_network(1, set_agd_arm(smoking, studyn, trtc, r = r, n = n)), msg)
expect_error(combine_network(set_agd_arm(smoking, studyn, trtc, r = r, n = n), 1), msg)
})
# Dummy data
agd_arm <- tibble(
studyn = c(1, 1, 2, 2, 2),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(1, 2, 1, 2, 3),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(1, 2, 1, 2, 2),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
y = rnorm(5),
se = runif(5)
)
net_a_a <- set_agd_arm(agd_arm, studyf, trtf, y = y, se = se)
net_a_a2 <- set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se)
agd_contrast <- tibble(
studyn = c(3, 3, 3),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(2, 3, 4),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(2, 2, 3),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
tclassn_bad = c(3, 3, 3),
y = c(NA, rnorm(2)),
se = c(0.5, 1, 1)
)
net_a_c <- set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se)
net_a_c2 <- set_agd_contrast(agd_contrast, studyf2, trtf2, y = y, se = se)
ipd <- tibble(
studyn = c(4, 4, 4, 5, 5),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(1, 2, 3, 1, 4),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(1, 2, 2, 1, 3),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
tclassn_bad = c(1, 3, 3, 3, 4),
y = rnorm(5)
)
net_i <- set_ipd(ipd, studyf, trtf, y = y)
net_i2 <- set_ipd(ipd, studyf2, trtf2, y = y)
test_that("combine_network produces combined treatment, class, and study factors", {
# Note: original_levels attribute unset because levels differ in sub-networks
c1 <- combine_network(net_a_a, net_i)
expect_equal(c1$treatments, .default(factor(LETTERS[1:4])))
expect_equal(levels(c1$agd_arm$.trt), LETTERS[1:4])
expect_equal(levels(c1$ipd$.trt), LETTERS[1:4])
expect_equal(c1$studies, factor(letters[c(1, 2, 4, 5)]))
expect_equal(levels(c1$agd_arm$.study), letters[c(1, 2, 4, 5)])
expect_equal(levels(c1$ipd$.study), letters[c(1, 2, 4, 5)])
c2 <- combine_network(net_a_a, net_i, net_a_c)
expect_equal(c2$treatments, .default(factor(LETTERS[c(2, 1, 3, 4)],
levels = LETTERS[c(2, 1, 3, 4)])))
expect_equal(levels(c2$agd_arm$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2$agd_contrast$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2$ipd$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(c2$studies, factor(letters[1:5]))
expect_equal(levels(c2$agd_arm$.study), letters[1:5])
expect_equal(levels(c2$agd_contrast$.study), letters[1:5])
expect_equal(levels(c2$ipd$.study), letters[1:5])
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc)
)
# Reference trt is A
expect_equal(c1_classed$treatments, .default(factor(LETTERS[1:4])))
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[1:4])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[1:4])
expect_equal(c1_classed$studies, factor(letters[c(1, 2, 4, 5)]))
expect_equal(levels(c1_classed$agd_arm$.study), letters[c(1, 2, 4, 5)])
expect_equal(levels(c1_classed$ipd$.study), letters[c(1, 2, 4, 5)])
expect_equal(c1_classed$classes, factor(letters[c(1, 2, 2, 3)]))
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[1:3])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[1:3])
c2_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassc)
)
# Reference treatment is B
expect_equal(c2_classed$treatments, .default(factor(LETTERS[c(2, 1, 3, 4)],
levels = LETTERS[c(2, 1, 3, 4)])))
expect_equal(levels(c2_classed$agd_arm$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2_classed$agd_contrast$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2_classed$ipd$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(c2_classed$studies, factor(letters[1:5]))
expect_equal(levels(c2_classed$agd_arm$.study), letters[1:5])
expect_equal(levels(c2_classed$agd_contrast$.study), letters[1:5])
expect_equal(levels(c2_classed$ipd$.study), letters[1:5])
expect_equal(c2_classed$classes, factor(letters[c(2, 1, 2, 3)], levels = c(letters[c(2, 1, 3)])))
expect_equal(levels(c2_classed$agd_arm$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c2_classed$ipd$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c2_classed$agd_contrast$.trtclass), letters[c(2, 1, 3)])
})
test_that("combine_network produces combined treatment, class, and study factors from explicit factors", {
# Note: original_levels attribute now set because levels are explicitly the same in sub-networks
c1 <- combine_network(net_a_a2, net_i2)
c1_trtf <- .default(factor(LETTERS[c(1, 4:2)], levels = LETTERS[c(1, 4:2)]))
attr(c1_trtf, "original_levels") <- LETTERS[4:1]
expect_equal(c1$treatments, c1_trtf)
expect_equal(levels(c1$agd_arm$.trt), LETTERS[c(1, 4:2)])
expect_equal(levels(c1$ipd$.trt), LETTERS[c(1, 4:2)])
c1_studyf <- factor(letters[c(5, 4, 2, 1)], levels = letters[c(5, 4, 2, 1)])
attr(c1_studyf, "original_levels") <- letters[5:1]
expect_equal(c1$studies, c1_studyf)
expect_equal(levels(c1$agd_arm$.study), letters[c(5, 4, 2, 1)])
expect_equal(levels(c1$ipd$.study), letters[c(5, 4, 2, 1)])
c2 <- combine_network(net_a_a2, net_i2, net_a_c2)
c2_trtf <- .default(factor(LETTERS[c(2, 4, 3, 1)], levels = LETTERS[c(2, 4, 3, 1)]))
attr(c2_trtf, "original_levels") <- LETTERS[4:1]
expect_equal(c2$treatments, c2_trtf)
expect_equal(levels(c2$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
c2_studyf <- factor(letters[5:1], levels = letters[5:1])
attr(c2_studyf, "original_levels") <- letters[5:1]
expect_equal(c2$studies, c2_studyf)
expect_equal(levels(c2$agd_arm$.study), letters[5:1])
expect_equal(levels(c2$agd_contrast$.study), letters[5:1])
expect_equal(levels(c2$ipd$.study), letters[5:1])
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se, trt_class = tclassf2),
set_ipd(ipd, studyf2, trtf2, y = y, trt_class = tclassf2)
)
# Reference trt is A
expect_equal(c1_classed$treatments, c1_trtf)
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[c(1, 4:2)])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[c(1, 4:2)])
expect_equal(c1_classed$studies, c1_studyf)
expect_equal(levels(c1_classed$agd_arm$.study), letters[c(5, 4, 2, 1)])
expect_equal(levels(c1_classed$ipd$.study), letters[c(5, 4, 2, 1)])
c1_classf <- factor(letters[c(1, 3, 2, 2)], levels = letters[c(1, 3, 2)])
attr(c1_classf, "original_levels") <- letters[3:1]
expect_equal(c1_classed$classes, c1_classf)
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[c(1, 3, 2)])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[c(1, 3, 2)])
c2_classed <- combine_network(
set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se, trt_class = tclassf2),
set_ipd(ipd, studyf2, trtf2, y = y, trt_class = tclassf2),
set_agd_contrast(agd_contrast, studyf2, trtf2, y = y, se = se, trt_class = tclassf2)
)
# Reference treatment is B
expect_equal(c2_classed$treatments, c2_trtf)
expect_equal(levels(c2_classed$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2_classed$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2_classed$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(c2_classed$studies, c2_studyf)
expect_equal(levels(c2_classed$agd_arm$.study), letters[5:1])
expect_equal(levels(c2_classed$agd_contrast$.study), letters[5:1])
expect_equal(levels(c2_classed$ipd$.study), letters[5:1])
c2_classf <- factor(letters[c(2, 3, 2, 1)], levels = c(letters[c(2, 3, 1)]))
attr(c2_classf, "original_levels") <- letters[3:1]
expect_equal(c2_classed$classes, c2_classf)
expect_equal(levels(c2_classed$agd_arm$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c2_classed$ipd$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c2_classed$agd_contrast$.trtclass), letters[c(2, 3, 1)])
# Check that unused levels are dropped
c3_classed <- combine_network(
set_agd_arm(agd_arm,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y, se = se,
trt_class = forcats::fct_expand(tclassf2, "zzz")),
set_ipd(ipd,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y,
trt_class = forcats::fct_expand(tclassf2, "zzz")),
set_agd_contrast(agd_contrast,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y, se = se,
trt_class = forcats::fct_expand(tclassf2, "zzz")))
# Reference treatment is B
c3_trtf <- c2_trtf
attr(c3_trtf, "original_levels") <- c(attr(c3_trtf, "original_levels"), "yyy")
expect_equal(c3_classed$treatments, c3_trtf)
expect_equal(levels(c3_classed$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c3_classed$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c3_classed$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
c3_studyf <- c2_studyf
attr(c3_studyf, "original_levels") <- c(attr(c3_studyf, "original_levels"), "xxx")
expect_equal(c3_classed$studies, c3_studyf)
expect_equal(levels(c3_classed$agd_arm$.study), letters[5:1])
expect_equal(levels(c3_classed$agd_contrast$.study), letters[5:1])
expect_equal(levels(c3_classed$ipd$.study), letters[5:1])
c3_classf <- factor(letters[c(2, 3, 2, 1)], levels = c(letters[c(2, 3, 1)]))
attr(c3_classf, "original_levels") <- c(letters[3:1], "zzz")
expect_equal(c3_classed$classes, c3_classf)
expect_equal(levels(c3_classed$agd_arm$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c3_classed$ipd$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c3_classed$agd_contrast$.trtclass), letters[c(2, 3, 1)])
})
test_that("combine_network can set alternative trt_ref", {
c1 <- combine_network(net_a_a, net_i, net_a_c, trt_ref = "C")
expect_equal(c1$treatments, factor(LETTERS[c(3, 1, 2, 4)], levels = LETTERS[c(3, 1, 2, 4)]))
expect_equal(levels(c1$agd_arm$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1$agd_contrast$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1$ipd$.trt), LETTERS[c(3, 1, 2, 4)])
expect_error(combine_network(net_a_a, net_i, net_a_c, trt_ref = 2),
"does not match a treatment.*Suitable values are: A, B, C, D")
expect_error(combine_network(net_a_a, net_a_c,
set_ipd(mutate(ipd, trtf = factor(LETTERS[3:7])),
studyf, trtf, y = y),
trt_ref = 2),
"does not match a treatment.*Suitable values are: A, B, C, D, E, \\.\\.\\.")
expect_error(combine_network(net_a_a, net_a_c,
set_ipd(mutate(ipd, studyf = factor("a")),
studyf, trtf, y = y)),
"Studies with same label found in multiple data sources: a")
expect_error(combine_network(net_a_a, net_a_a),
"Studies with same label found in multiple data sources: a, b")
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassc),
trt_ref = "C"
)
expect_equal(c1_classed$treatments, factor(LETTERS[c(3, 1, 2, 4)], levels = LETTERS[c(3, 1, 2, 4)]))
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1_classed$agd_contrast$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(c1_classed$classes, factor(letters[c(2, 1, 2, 3)], levels = letters[c(2, 1, 3)]))
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c1_classed$agd_contrast$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[c(2, 1, 3)])
})
test_that("combine_network error if outcomes do not match for same data source type", {
m <- "Multiple outcome types present"
dat_a <- tibble(study = 1, trt = 1:2, r = 1, n = 1, y = 1, se = 1)
dat_b <- tibble(study = 2, trt = 2:3, r = 1, n = 1, y = 1, se = 1)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_ipd(dat_b, study, trt, y = y)), m)
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = r, n = n),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
})
test_that("combine_network error if mismatch outcomes across data types", {
m <- "Combining.+not supported"
dat_a <- tibble(study = 1, trt = 1:2, r = 1, n = 1, E = 1, y = 1, se = 1)
dat_b <- tibble(study = 2, trt = 2:3, r = 1, n = 1, E = 1, y = 1, se = 1)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(n, r, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(n, r, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = r, n = n)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r, E = E),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = r, n = n)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = multi(n, r, inclusive = TRUE))), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_agd_arm(dat_b, study, trt, r = multi(n, r, inclusive = TRUE))), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = r, E = E)), m)
})
test_that("combine_network error if multinomial outcomes mismatched", {
dat_a <- tibble(study = 1, trt = 1:2, a = 1, b = 1, c = 0)
dat_b <- tibble(study = 2, trt = 2:3, a = 1, b = 1, c = 0)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
# Check combining ordered and competing outcomes is disallowed, when competing are implemented
})
test_that("combine_network error if treatment classes do not match across sources", {
m <- "Treatment present in more than one class"
expect_error(combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassn),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassn_bad)), m)
expect_error(combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassn),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassn_bad),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassn_bad)), m)
})
test_that("combine_network warns if not all sources have treatment classes", {
expect_warning(combine_network(net_a_a,
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc)),
"Not all data sources have defined treatment classes")
})
test_that("combine_network works combining survival outcomes from same type of ipd/agd source", {
expect_equivalent(combine_network(set_agd_surv(filter(ndmm_agd, study == "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs),
set_agd_surv(filter(ndmm_agd, study != "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs))$agd_arm,
set_agd_surv(ndmm_agd, studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs)$agd_arm)
expect_equivalent(combine_network(set_ipd(filter(ndmm_agd, study == "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status)),
set_ipd(filter(ndmm_agd, study != "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status)))$ipd,
set_ipd(ndmm_agd, studyf, trtf,
Surv = Surv(eventtime, status))$ipd)
})
test_that("combine_network respects mlnma_data objects with integration points", {
ipd_net <- set_ipd(plaque_psoriasis_ipd, study = studyc, trt = trtc, r = pasi75)
agd_net1 <- set_agd_arm(
filter(plaque_psoriasis_agd, studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2))
agd_net2 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
)
agd_net3 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2))
expect_s3_class(combine_network(agd_net1),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net1),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net1, agd_net3),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(agd_net1, agd_net3),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(
expect_message(combine_network(agd_net1, agd_net2),
"Dropping integration points"),
"nma_data", exact = TRUE)
expect_s3_class(
expect_message(combine_network(ipd_net, agd_net1, agd_net2),
"Dropping integration points"),
"nma_data", exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net2), "nma_data")
agd_net4 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd))
expect_error(combine_network(agd_net1, agd_net4), "different sets of covariates")
expect_error(combine_network(ipd_net, agd_net1, agd_net4), "different sets of covariates")
agd_net5 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2),
n_int = 8)
expect_error(combine_network(agd_net1, agd_net5), "different numbers of integration points")
expect_error(combine_network(ipd_net, agd_net1, agd_net5), "different numbers of integration points")
})
dmphillippo/multinma documentation built on April 12, 2025, 11:41 a.m.
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library(multinma)
library(dplyr)
test_that("combine_network error if not passed nma_data objects", {
msg <- "Expecting to combine objects of class `nma_data`, created using set_\\* functions"
expect_error(combine_network(1), msg)
expect_error(combine_network(1, 2), msg)
expect_error(combine_network(1, set_agd_arm(smoking, studyn, trtc, r = r, n = n)), msg)
expect_error(combine_network(set_agd_arm(smoking, studyn, trtc, r = r, n = n), 1), msg)
})
# Dummy data
agd_arm <- tibble(
studyn = c(1, 1, 2, 2, 2),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(1, 2, 1, 2, 3),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(1, 2, 1, 2, 2),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
y = rnorm(5),
se = runif(5)
)
net_a_a <- set_agd_arm(agd_arm, studyf, trtf, y = y, se = se)
net_a_a2 <- set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se)
agd_contrast <- tibble(
studyn = c(3, 3, 3),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(2, 3, 4),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(2, 2, 3),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
tclassn_bad = c(3, 3, 3),
y = c(NA, rnorm(2)),
se = c(0.5, 1, 1)
)
net_a_c <- set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se)
net_a_c2 <- set_agd_contrast(agd_contrast, studyf2, trtf2, y = y, se = se)
ipd <- tibble(
studyn = c(4, 4, 4, 5, 5),
studyc = letters[studyn],
studyf = factor(studyc),
studyf2 = factor(studyc, levels = letters[5:1]),
trtn = c(1, 2, 3, 1, 4),
trtc = LETTERS[trtn],
trtf = factor(trtc),
trtf2 = factor(trtc, levels = LETTERS[4:1]),
tclassn = c(1, 2, 2, 1, 3),
tclassc = letters[tclassn],
tclassf = factor(tclassc, levels = letters[1:3]),
tclassf2 = factor(tclassc, levels = letters[3:1]),
tclassn_bad = c(1, 3, 3, 3, 4),
y = rnorm(5)
)
net_i <- set_ipd(ipd, studyf, trtf, y = y)
net_i2 <- set_ipd(ipd, studyf2, trtf2, y = y)
test_that("combine_network produces combined treatment, class, and study factors", {
# Note: original_levels attribute unset because levels differ in sub-networks
c1 <- combine_network(net_a_a, net_i)
expect_equal(c1$treatments, .default(factor(LETTERS[1:4])))
expect_equal(levels(c1$agd_arm$.trt), LETTERS[1:4])
expect_equal(levels(c1$ipd$.trt), LETTERS[1:4])
expect_equal(c1$studies, factor(letters[c(1, 2, 4, 5)]))
expect_equal(levels(c1$agd_arm$.study), letters[c(1, 2, 4, 5)])
expect_equal(levels(c1$ipd$.study), letters[c(1, 2, 4, 5)])
c2 <- combine_network(net_a_a, net_i, net_a_c)
expect_equal(c2$treatments, .default(factor(LETTERS[c(2, 1, 3, 4)],
levels = LETTERS[c(2, 1, 3, 4)])))
expect_equal(levels(c2$agd_arm$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2$agd_contrast$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2$ipd$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(c2$studies, factor(letters[1:5]))
expect_equal(levels(c2$agd_arm$.study), letters[1:5])
expect_equal(levels(c2$agd_contrast$.study), letters[1:5])
expect_equal(levels(c2$ipd$.study), letters[1:5])
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc)
)
# Reference trt is A
expect_equal(c1_classed$treatments, .default(factor(LETTERS[1:4])))
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[1:4])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[1:4])
expect_equal(c1_classed$studies, factor(letters[c(1, 2, 4, 5)]))
expect_equal(levels(c1_classed$agd_arm$.study), letters[c(1, 2, 4, 5)])
expect_equal(levels(c1_classed$ipd$.study), letters[c(1, 2, 4, 5)])
expect_equal(c1_classed$classes, factor(letters[c(1, 2, 2, 3)]))
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[1:3])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[1:3])
c2_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassc)
)
# Reference treatment is B
expect_equal(c2_classed$treatments, .default(factor(LETTERS[c(2, 1, 3, 4)],
levels = LETTERS[c(2, 1, 3, 4)])))
expect_equal(levels(c2_classed$agd_arm$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2_classed$agd_contrast$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(levels(c2_classed$ipd$.trt), LETTERS[c(2, 1, 3, 4)])
expect_equal(c2_classed$studies, factor(letters[1:5]))
expect_equal(levels(c2_classed$agd_arm$.study), letters[1:5])
expect_equal(levels(c2_classed$agd_contrast$.study), letters[1:5])
expect_equal(levels(c2_classed$ipd$.study), letters[1:5])
expect_equal(c2_classed$classes, factor(letters[c(2, 1, 2, 3)], levels = c(letters[c(2, 1, 3)])))
expect_equal(levels(c2_classed$agd_arm$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c2_classed$ipd$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c2_classed$agd_contrast$.trtclass), letters[c(2, 1, 3)])
})
test_that("combine_network produces combined treatment, class, and study factors from explicit factors", {
# Note: original_levels attribute now set because levels are explicitly the same in sub-networks
c1 <- combine_network(net_a_a2, net_i2)
c1_trtf <- .default(factor(LETTERS[c(1, 4:2)], levels = LETTERS[c(1, 4:2)]))
attr(c1_trtf, "original_levels") <- LETTERS[4:1]
expect_equal(c1$treatments, c1_trtf)
expect_equal(levels(c1$agd_arm$.trt), LETTERS[c(1, 4:2)])
expect_equal(levels(c1$ipd$.trt), LETTERS[c(1, 4:2)])
c1_studyf <- factor(letters[c(5, 4, 2, 1)], levels = letters[c(5, 4, 2, 1)])
attr(c1_studyf, "original_levels") <- letters[5:1]
expect_equal(c1$studies, c1_studyf)
expect_equal(levels(c1$agd_arm$.study), letters[c(5, 4, 2, 1)])
expect_equal(levels(c1$ipd$.study), letters[c(5, 4, 2, 1)])
c2 <- combine_network(net_a_a2, net_i2, net_a_c2)
c2_trtf <- .default(factor(LETTERS[c(2, 4, 3, 1)], levels = LETTERS[c(2, 4, 3, 1)]))
attr(c2_trtf, "original_levels") <- LETTERS[4:1]
expect_equal(c2$treatments, c2_trtf)
expect_equal(levels(c2$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
c2_studyf <- factor(letters[5:1], levels = letters[5:1])
attr(c2_studyf, "original_levels") <- letters[5:1]
expect_equal(c2$studies, c2_studyf)
expect_equal(levels(c2$agd_arm$.study), letters[5:1])
expect_equal(levels(c2$agd_contrast$.study), letters[5:1])
expect_equal(levels(c2$ipd$.study), letters[5:1])
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se, trt_class = tclassf2),
set_ipd(ipd, studyf2, trtf2, y = y, trt_class = tclassf2)
)
# Reference trt is A
expect_equal(c1_classed$treatments, c1_trtf)
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[c(1, 4:2)])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[c(1, 4:2)])
expect_equal(c1_classed$studies, c1_studyf)
expect_equal(levels(c1_classed$agd_arm$.study), letters[c(5, 4, 2, 1)])
expect_equal(levels(c1_classed$ipd$.study), letters[c(5, 4, 2, 1)])
c1_classf <- factor(letters[c(1, 3, 2, 2)], levels = letters[c(1, 3, 2)])
attr(c1_classf, "original_levels") <- letters[3:1]
expect_equal(c1_classed$classes, c1_classf)
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[c(1, 3, 2)])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[c(1, 3, 2)])
c2_classed <- combine_network(
set_agd_arm(agd_arm, studyf2, trtf2, y = y, se = se, trt_class = tclassf2),
set_ipd(ipd, studyf2, trtf2, y = y, trt_class = tclassf2),
set_agd_contrast(agd_contrast, studyf2, trtf2, y = y, se = se, trt_class = tclassf2)
)
# Reference treatment is B
expect_equal(c2_classed$treatments, c2_trtf)
expect_equal(levels(c2_classed$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2_classed$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c2_classed$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(c2_classed$studies, c2_studyf)
expect_equal(levels(c2_classed$agd_arm$.study), letters[5:1])
expect_equal(levels(c2_classed$agd_contrast$.study), letters[5:1])
expect_equal(levels(c2_classed$ipd$.study), letters[5:1])
c2_classf <- factor(letters[c(2, 3, 2, 1)], levels = c(letters[c(2, 3, 1)]))
attr(c2_classf, "original_levels") <- letters[3:1]
expect_equal(c2_classed$classes, c2_classf)
expect_equal(levels(c2_classed$agd_arm$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c2_classed$ipd$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c2_classed$agd_contrast$.trtclass), letters[c(2, 3, 1)])
# Check that unused levels are dropped
c3_classed <- combine_network(
set_agd_arm(agd_arm,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y, se = se,
trt_class = forcats::fct_expand(tclassf2, "zzz")),
set_ipd(ipd,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y,
trt_class = forcats::fct_expand(tclassf2, "zzz")),
set_agd_contrast(agd_contrast,
forcats::fct_expand(studyf2, "xxx"),
forcats::fct_expand(trtf2, "yyy"),
y = y, se = se,
trt_class = forcats::fct_expand(tclassf2, "zzz")))
# Reference treatment is B
c3_trtf <- c2_trtf
attr(c3_trtf, "original_levels") <- c(attr(c3_trtf, "original_levels"), "yyy")
expect_equal(c3_classed$treatments, c3_trtf)
expect_equal(levels(c3_classed$agd_arm$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c3_classed$agd_contrast$.trt), LETTERS[c(2, 4, 3, 1)])
expect_equal(levels(c3_classed$ipd$.trt), LETTERS[c(2, 4, 3, 1)])
c3_studyf <- c2_studyf
attr(c3_studyf, "original_levels") <- c(attr(c3_studyf, "original_levels"), "xxx")
expect_equal(c3_classed$studies, c3_studyf)
expect_equal(levels(c3_classed$agd_arm$.study), letters[5:1])
expect_equal(levels(c3_classed$agd_contrast$.study), letters[5:1])
expect_equal(levels(c3_classed$ipd$.study), letters[5:1])
c3_classf <- factor(letters[c(2, 3, 2, 1)], levels = c(letters[c(2, 3, 1)]))
attr(c3_classf, "original_levels") <- c(letters[3:1], "zzz")
expect_equal(c3_classed$classes, c3_classf)
expect_equal(levels(c3_classed$agd_arm$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c3_classed$ipd$.trtclass), letters[c(2, 3, 1)])
expect_equal(levels(c3_classed$agd_contrast$.trtclass), letters[c(2, 3, 1)])
})
test_that("combine_network can set alternative trt_ref", {
c1 <- combine_network(net_a_a, net_i, net_a_c, trt_ref = "C")
expect_equal(c1$treatments, factor(LETTERS[c(3, 1, 2, 4)], levels = LETTERS[c(3, 1, 2, 4)]))
expect_equal(levels(c1$agd_arm$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1$agd_contrast$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1$ipd$.trt), LETTERS[c(3, 1, 2, 4)])
expect_error(combine_network(net_a_a, net_i, net_a_c, trt_ref = 2),
"does not match a treatment.*Suitable values are: A, B, C, D")
expect_error(combine_network(net_a_a, net_a_c,
set_ipd(mutate(ipd, trtf = factor(LETTERS[3:7])),
studyf, trtf, y = y),
trt_ref = 2),
"does not match a treatment.*Suitable values are: A, B, C, D, E, \\.\\.\\.")
expect_error(combine_network(net_a_a, net_a_c,
set_ipd(mutate(ipd, studyf = factor("a")),
studyf, trtf, y = y)),
"Studies with same label found in multiple data sources: a")
expect_error(combine_network(net_a_a, net_a_a),
"Studies with same label found in multiple data sources: a, b")
c1_classed <- combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassc),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassc),
trt_ref = "C"
)
expect_equal(c1_classed$treatments, factor(LETTERS[c(3, 1, 2, 4)], levels = LETTERS[c(3, 1, 2, 4)]))
expect_equal(levels(c1_classed$agd_arm$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1_classed$agd_contrast$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(levels(c1_classed$ipd$.trt), LETTERS[c(3, 1, 2, 4)])
expect_equal(c1_classed$classes, factor(letters[c(2, 1, 2, 3)], levels = letters[c(2, 1, 3)]))
expect_equal(levels(c1_classed$agd_arm$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c1_classed$agd_contrast$.trtclass), letters[c(2, 1, 3)])
expect_equal(levels(c1_classed$ipd$.trtclass), letters[c(2, 1, 3)])
})
test_that("combine_network error if outcomes do not match for same data source type", {
m <- "Multiple outcome types present"
dat_a <- tibble(study = 1, trt = 1:2, r = 1, n = 1, y = 1, se = 1)
dat_b <- tibble(study = 2, trt = 2:3, r = 1, n = 1, y = 1, se = 1)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_ipd(dat_b, study, trt, y = y)), m)
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = r, n = n),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
})
test_that("combine_network error if mismatch outcomes across data types", {
m <- "Combining.+not supported"
dat_a <- tibble(study = 1, trt = 1:2, r = 1, n = 1, E = 1, y = 1, se = 1)
dat_b <- tibble(study = 2, trt = 2:3, r = 1, n = 1, E = 1, y = 1, se = 1)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(n, r, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(n, r, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = r, n = n)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r, E = E),
set_agd_arm(dat_b, study, trt, y = y, se = se)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = r, n = n)), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = multi(n, r, inclusive = TRUE))), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = r),
set_agd_arm(dat_b, study, trt, r = multi(n, r, inclusive = TRUE))), m)
expect_error(combine_network(set_ipd(dat_a, study, trt, y = y),
set_agd_arm(dat_b, study, trt, r = r, E = E)), m)
})
test_that("combine_network error if multinomial outcomes mismatched", {
dat_a <- tibble(study = 1, trt = 1:2, a = 1, b = 1, c = 0)
dat_b <- tibble(study = 2, trt = 2:3, a = 1, b = 1, c = 0)
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_ipd(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_ipd(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(a, b, inclusive = TRUE))),
"different numbers of categories")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(b, a, c, inclusive = TRUE))),
"different category labels")
expect_error(combine_network(set_agd_arm(dat_a, study, trt, r = multi(a, b, c, inclusive = TRUE)),
set_agd_arm(dat_b, study, trt, r = multi(A = a, B = b, C = c, inclusive = TRUE))),
"different category labels")
# Check combining ordered and competing outcomes is disallowed, when competing are implemented
})
test_that("combine_network error if treatment classes do not match across sources", {
m <- "Treatment present in more than one class"
expect_error(combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassn),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassn_bad)), m)
expect_error(combine_network(
set_agd_arm(agd_arm, studyf, trtf, y = y, se = se, trt_class = tclassn),
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassn_bad),
set_agd_contrast(agd_contrast, studyf, trtf, y = y, se = se, trt_class = tclassn_bad)), m)
})
test_that("combine_network warns if not all sources have treatment classes", {
expect_warning(combine_network(net_a_a,
set_ipd(ipd, studyf, trtf, y = y, trt_class = tclassc)),
"Not all data sources have defined treatment classes")
})
test_that("combine_network works combining survival outcomes from same type of ipd/agd source", {
expect_equivalent(combine_network(set_agd_surv(filter(ndmm_agd, study == "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs),
set_agd_surv(filter(ndmm_agd, study != "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs))$agd_arm,
set_agd_surv(ndmm_agd, studyf, trtf,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs)$agd_arm)
expect_equivalent(combine_network(set_ipd(filter(ndmm_agd, study == "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status)),
set_ipd(filter(ndmm_agd, study != "Morgan2012"), studyf, trtf,
Surv = Surv(eventtime, status)))$ipd,
set_ipd(ndmm_agd, studyf, trtf,
Surv = Surv(eventtime, status))$ipd)
})
test_that("combine_network respects mlnma_data objects with integration points", {
ipd_net <- set_ipd(plaque_psoriasis_ipd, study = studyc, trt = trtc, r = pasi75)
agd_net1 <- set_agd_arm(
filter(plaque_psoriasis_agd, studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2))
agd_net2 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
)
agd_net3 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2))
expect_s3_class(combine_network(agd_net1),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net1),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net1, agd_net3),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(combine_network(agd_net1, agd_net3),
c("mlnmr_data", "nma_data"), exact = TRUE)
expect_s3_class(
expect_message(combine_network(agd_net1, agd_net2),
"Dropping integration points"),
"nma_data", exact = TRUE)
expect_s3_class(
expect_message(combine_network(ipd_net, agd_net1, agd_net2),
"Dropping integration points"),
"nma_data", exact = TRUE)
expect_s3_class(combine_network(ipd_net, agd_net2), "nma_data")
agd_net4 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd))
expect_error(combine_network(agd_net1, agd_net4), "different sets of covariates")
expect_error(combine_network(ipd_net, agd_net1, agd_net4), "different sets of covariates")
agd_net5 <- set_agd_arm(
filter(plaque_psoriasis_agd, !studyc %in% c("FEATURE", "FIXTURE", "JUNCTURE")),
study = studyc, trt = trtc, r = pasi75_r, n = pasi75_n
) %>%
add_integration(durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd), cor = diag(2),
n_int = 8)
expect_error(combine_network(agd_net1, agd_net5), "different numbers of integration points")
expect_error(combine_network(ipd_net, agd_net1, agd_net5), "different numbers of integration points")
})
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