tests/testthat/test-independent-testing-prepare_ae_summary.R
In Merck/metalite.ae: Adverse Events Analysis Using 'metalite'
# Test plan
#
# The output is a list.
# Test each component:
# - `population`: match the expectation.
# - `parameter`: match the expectation.
# - `n`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `order`: match the expectation.
# - `group`: match the expectation.
# - `reference_group`: match the expectation.
# - `prop`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `diff`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `name`: match the expectation.
adsl <- r2rtf::r2rtf_adsl
adae <- r2rtf::r2rtf_adae |> dplyr::mutate(TRT01A = TRTA)
adsl |>
dplyr::group_by(TRT01P) |>
dplyr::summarize(n = dplyr::n())
sum_par <- "any;rel;nonser;ser;ser0rel;dth;dtc0rel;disc;disc0drel;disc0ser;disc0ser0rel"
plan <- plan(
analysis = "ae_summary",
population = "apat",
observation = "trtemfl",
parameter = sum_par,
pilot = TRUE
)
adsl$TRT01P <- factor(adsl$TRT01P, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
adsl$TRT01A <- factor(adsl$TRT01A, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
adae$TRT01A <- factor(adae$TRT01A, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
meta <- meta_adam(
observation = adae,
population = adsl
)
plan_pilot <- plan |>
subset(pilot) |>
dplyr::mutate(display_total = analysis == "ae_summary")
meta <- meta |> define_plan(plan_pilot)
meta <- meta |>
define_population(
name = "apat",
group = "TRT01A",
subset = ITTFL == "Y",
label = "(All Participants as Treated)"
)
meta <- meta |> define_observation(
name = "trtemfl",
group = "TRT01A",
var = "AEDECOD",
subset = TRTEMFL == "Y",
label = "Treatment emergent"
)
meta_example <- meta |>
define_parameter(name = "any") |>
define_parameter(
name = "rel",
subset = AEREL == "RELATED",
label = "drug-related adverse events"
) |>
define_parameter(
name = "nonser",
subset = AESER != "Y" | AESER == "",
label = "non-serious adverse events"
) |>
define_parameter(
name = "ser",
subset = AESER == "Y",
label = "serious adverse events"
) |>
define_parameter(
name = "ser0rel",
subset = AESER == "Y" & AEREL == "RELATED",
label = "serious drug-related adverse events"
) |>
define_parameter(
name = "dth",
subset = AESDTH == "Y",
label = "adverse events result in death"
) |>
define_parameter(
name = "dtc0rel",
subset = AESDTH == "Y" & AEREL == "RELATED",
label = "drug-related adverse events result in death"
) |>
define_parameter(
name = "disc",
subset = toupper(AEACN) == "DRUG WITHDRAWN",
label = "adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0drel",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AEREL == "RELATED",
label = "drug-related adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0ser",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y",
label = "serious adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0ser0rel",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y" & AEREL == "RELATED",
label = "serious drug-related adverse events resulting in discontinuation"
)
meta_example <- meta_example |>
define_analysis(
name = "ae_summary",
title = "Adverse Event Summary"
)
meta_example <- meta_example |> meta_build()
meta_example$plan <- meta_example$plan |>
dplyr::mutate(output_report = spec_filename(meta))
test_meta <- meta_example
# usethis::use_data(test_meta, overwrite = TRUE)
x <- prepare_ae_summary(
meta = test_meta,
population = "apat",
observation = "trtemfl",
parameter = sum_par
)
test_that("output from prepare_ae_summary is a list", {
expect_true("outdata" %in% class(x))
})
xx <- tibble::tibble(cbind(x$name, x$n)) |> dplyr::mutate(x = dplyr::row_number())
xxp <- x$prop |> dplyr::mutate(x = dplyr::row_number())
xxpd <- x$diff
xxpd <- xxpd[order(as.numeric(row.names(xxpd))), ] |> dplyr::mutate(x = dplyr::row_number())
rownames(xxp) <- NULL
rownames(xxpd) <- NULL
adsl_tot <- adsl |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adsl_tot <- rbind(adsl, adsl_tot)
res_tot <- adsl_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_tot1 <- res_tot |>
dplyr::mutate(z = n) |>
dplyr::select(-c(n))
test_that("Parameter matching", {
expect_equal(x$parameter, sum_par)
})
test_that("Group matching", {
expect_equal(x$group, c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose", "Total"))
})
test_that("reference_group matching", {
expect_equal(levels(adsl$TRT01A)[x$reference_group], "Placebo")
})
test_that("Participants in population", {
res <- res_tot |>
dplyr::mutate(x = paste0("n_", dplyr::row_number()))
res1 <- res |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
expect_equal(
xx |> dplyr::filter(x == 1) |> dplyr::select(-c("x$name", x)),
res1 |> dplyr::select(-c(ITTFL))
)
# Population count
expect_equal(
xx |> dplyr::filter(x == 1) |> dplyr::select(-c("x$name", x)),
tibble::tibble(x$n_pop)
)
# Name matches
expect_equal(x$name[1], "Participants in population")
})
test_that("with one or more adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae2 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop2 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff2 <- res_prop2 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 2) |> dplyr::select(-c("x$name", x)), res_ae2 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 2) |> dplyr::select(-c(x)), data.frame(res_prop2) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 2) |> dplyr::select(-c(x)), data.frame(res_diff2) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[2], "with one or more adverse events")
})
test_that("with one or more adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_no <- dplyr::full_join(
res_tot1, # Merge with adsl to get percentage
res_ae,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
) |> dplyr::mutate(p = z - n)
res_no <- res_no |>
dplyr::mutate(pct = formatC(100 * p / z, digits = 13, format = "f", flag = "0"))
res_no$pct <- as.numeric(res_no$pct)
res_no <- res_no |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae3 <- res_no |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = p,
values_fill = list(p = 0)
)
res_prop3 <- res_no |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff3 <- res_prop3 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 3) |> dplyr::select(-c("x$name", x)), res_ae3 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 3) |> dplyr::select(-c(x)), data.frame(res_prop3) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 3) |> dplyr::select(-c(x)), data.frame(res_diff3) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[3], "with no adverse events")
})
test_that("with drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
if (!"RELATED" %in% res_ae$AEREL) {
res_ae <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEREL = "RELATED", n = 0)
}
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae4 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop4 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff4 <- res_prop4 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 4) |> dplyr::select(-c("x$name", x)), res_ae4 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 4) |> dplyr::select(-c(x)), data.frame(res_prop4) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 4) |> dplyr::select(-c(x)), data.frame(res_diff4) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[4], "with drug-related{^a} adverse events")
})
test_that("non-serious adverse events count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y" & AESER != "Y" | AESER == "")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae5 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop5 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff5 <- res_prop5 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 5) |> dplyr::select(-c("x$name", x)), res_ae5 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 5) |> dplyr::select(-c(x)), data.frame(res_prop5) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 5) |> dplyr::select(-c(x)), data.frame(res_diff5) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[5], "with non-serious adverse events")
})
test_that("serious adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESER) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESER = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(AESER == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESER")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae6 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop6 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff6 <- res_prop6 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 6) |> dplyr::select(-c("x$name", x)), res_ae6 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 6) |> dplyr::select(-c(x)), data.frame(res_prop6) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 6) |> dplyr::select(-c(x)), data.frame(res_diff6) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[6], "with serious adverse events")
})
test_that("serious drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESER, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESER = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(AESER == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESER", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae7 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop7 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff7 <- res_prop7 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 7) |> dplyr::select(-c("x$name", x)), res_ae7 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 7) |> dplyr::select(-c(x)), data.frame(res_prop7) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 7) |> dplyr::select(-c(x)), data.frame(res_diff7) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[7], "with serious drug-related adverse events")
})
test_that("death count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESDTH) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESDTH = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(AESDTH == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESDTH")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae8 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop8 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff8 <- res_prop8 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 8) |> dplyr::select(-c("x$name", x)), res_ae8 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 8) |> dplyr::select(-c(x)), data.frame(res_prop8) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 8) |> dplyr::select(-c(x)), data.frame(res_diff8) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[8], "who died")
})
test_that("died due to drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESDTH, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESDTH = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(AESDTH == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESDTH", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae9 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop9 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff9 <- res_prop9 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 9) |> dplyr::select(-c("x$name", x)), res_ae9 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 9) |> dplyr::select(-c(x)), data.frame(res_prop9) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 9) |> dplyr::select(-c(x)), data.frame(res_diff9) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[9], "who died due to a drug-related adverse event")
})
test_that("discontinued due to an adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae10 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop10 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff10 <- res_prop10 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 10) |> dplyr::select(-c("x$name", x)), res_ae10 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 10) |> dplyr::select(-c(x)), data.frame(res_prop10) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 10) |> dplyr::select(-c(x)), data.frame(res_diff10) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[10], "discontinued any drug due to an adverse events")
})
test_that("discontinued due to drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae11 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop11 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff11 <- res_prop11 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 11) |> dplyr::select(-c("x$name", x)), res_ae11 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 11) |> dplyr::select(-c(x)), data.frame(res_prop11) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 11) |> dplyr::select(-c(x)), data.frame(res_diff11) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[11], "discontinued any drug due to a drug-related adverse events")
})
test_that("discontinued due to serious adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AESER) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AESER = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AESER")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae12 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop12 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff12 <- res_prop12 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 12) |> dplyr::select(-c("x$name", x)), res_ae12 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 12) |> dplyr::select(-c(x)), data.frame(res_prop12) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 12) |> dplyr::select(-c(x)), data.frame(res_diff12) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[12], "discontinued any drug due to a serious adverse event")
})
test_that("discontinued due to serious drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AESER, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AESER = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AESER", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae13 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop13 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff13 <- res_prop13 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 13) |> dplyr::select(-c("x$name", x)), res_ae13 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 13) |> dplyr::select(-c(x)), data.frame(res_prop13) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 13) |> dplyr::select(-c(x)), data.frame(res_diff13) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[13], "discontinued any drug due to a serious drug-related adverse event")
})
Merck/metalite.ae documentation built on Feb. 10, 2025, 5:03 p.m.
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# Test plan
#
# The output is a list.
# Test each component:
# - `population`: match the expectation.
# - `parameter`: match the expectation.
# - `n`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `order`: match the expectation.
# - `group`: match the expectation.
# - `reference_group`: match the expectation.
# - `prop`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `diff`: match that from SAS
# (take r2rtf::r2rtf_adsl and r2rtf::r2rtf_adae as an example).
# - `name`: match the expectation.
adsl <- r2rtf::r2rtf_adsl
adae <- r2rtf::r2rtf_adae |> dplyr::mutate(TRT01A = TRTA)
adsl |>
dplyr::group_by(TRT01P) |>
dplyr::summarize(n = dplyr::n())
sum_par <- "any;rel;nonser;ser;ser0rel;dth;dtc0rel;disc;disc0drel;disc0ser;disc0ser0rel"
plan <- plan(
analysis = "ae_summary",
population = "apat",
observation = "trtemfl",
parameter = sum_par,
pilot = TRUE
)
adsl$TRT01P <- factor(adsl$TRT01P, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
adsl$TRT01A <- factor(adsl$TRT01A, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
adae$TRT01A <- factor(adae$TRT01A, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))
meta <- meta_adam(
observation = adae,
population = adsl
)
plan_pilot <- plan |>
subset(pilot) |>
dplyr::mutate(display_total = analysis == "ae_summary")
meta <- meta |> define_plan(plan_pilot)
meta <- meta |>
define_population(
name = "apat",
group = "TRT01A",
subset = ITTFL == "Y",
label = "(All Participants as Treated)"
)
meta <- meta |> define_observation(
name = "trtemfl",
group = "TRT01A",
var = "AEDECOD",
subset = TRTEMFL == "Y",
label = "Treatment emergent"
)
meta_example <- meta |>
define_parameter(name = "any") |>
define_parameter(
name = "rel",
subset = AEREL == "RELATED",
label = "drug-related adverse events"
) |>
define_parameter(
name = "nonser",
subset = AESER != "Y" | AESER == "",
label = "non-serious adverse events"
) |>
define_parameter(
name = "ser",
subset = AESER == "Y",
label = "serious adverse events"
) |>
define_parameter(
name = "ser0rel",
subset = AESER == "Y" & AEREL == "RELATED",
label = "serious drug-related adverse events"
) |>
define_parameter(
name = "dth",
subset = AESDTH == "Y",
label = "adverse events result in death"
) |>
define_parameter(
name = "dtc0rel",
subset = AESDTH == "Y" & AEREL == "RELATED",
label = "drug-related adverse events result in death"
) |>
define_parameter(
name = "disc",
subset = toupper(AEACN) == "DRUG WITHDRAWN",
label = "adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0drel",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AEREL == "RELATED",
label = "drug-related adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0ser",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y",
label = "serious adverse events resulting in discontinuation"
) |>
define_parameter(
name = "disc0ser0rel",
subset = toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y" & AEREL == "RELATED",
label = "serious drug-related adverse events resulting in discontinuation"
)
meta_example <- meta_example |>
define_analysis(
name = "ae_summary",
title = "Adverse Event Summary"
)
meta_example <- meta_example |> meta_build()
meta_example$plan <- meta_example$plan |>
dplyr::mutate(output_report = spec_filename(meta))
test_meta <- meta_example
# usethis::use_data(test_meta, overwrite = TRUE)
x <- prepare_ae_summary(
meta = test_meta,
population = "apat",
observation = "trtemfl",
parameter = sum_par
)
test_that("output from prepare_ae_summary is a list", {
expect_true("outdata" %in% class(x))
})
xx <- tibble::tibble(cbind(x$name, x$n)) |> dplyr::mutate(x = dplyr::row_number())
xxp <- x$prop |> dplyr::mutate(x = dplyr::row_number())
xxpd <- x$diff
xxpd <- xxpd[order(as.numeric(row.names(xxpd))), ] |> dplyr::mutate(x = dplyr::row_number())
rownames(xxp) <- NULL
rownames(xxpd) <- NULL
adsl_tot <- adsl |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adsl_tot <- rbind(adsl, adsl_tot)
res_tot <- adsl_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_tot1 <- res_tot |>
dplyr::mutate(z = n) |>
dplyr::select(-c(n))
test_that("Parameter matching", {
expect_equal(x$parameter, sum_par)
})
test_that("Group matching", {
expect_equal(x$group, c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose", "Total"))
})
test_that("reference_group matching", {
expect_equal(levels(adsl$TRT01A)[x$reference_group], "Placebo")
})
test_that("Participants in population", {
res <- res_tot |>
dplyr::mutate(x = paste0("n_", dplyr::row_number()))
res1 <- res |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
expect_equal(
xx |> dplyr::filter(x == 1) |> dplyr::select(-c("x$name", x)),
res1 |> dplyr::select(-c(ITTFL))
)
# Population count
expect_equal(
xx |> dplyr::filter(x == 1) |> dplyr::select(-c("x$name", x)),
tibble::tibble(x$n_pop)
)
# Name matches
expect_equal(x$name[1], "Participants in population")
})
test_that("with one or more adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae2 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop2 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff2 <- res_prop2 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 2) |> dplyr::select(-c("x$name", x)), res_ae2 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 2) |> dplyr::select(-c(x)), data.frame(res_prop2) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 2) |> dplyr::select(-c(x)), data.frame(res_diff2) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[2], "with one or more adverse events")
})
test_that("with one or more adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_no <- dplyr::full_join(
res_tot1, # Merge with adsl to get percentage
res_ae,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
) |> dplyr::mutate(p = z - n)
res_no <- res_no |>
dplyr::mutate(pct = formatC(100 * p / z, digits = 13, format = "f", flag = "0"))
res_no$pct <- as.numeric(res_no$pct)
res_no <- res_no |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae3 <- res_no |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = p,
values_fill = list(p = 0)
)
res_prop3 <- res_no |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff3 <- res_prop3 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 3) |> dplyr::select(-c("x$name", x)), res_ae3 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 3) |> dplyr::select(-c(x)), data.frame(res_prop3) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 3) |> dplyr::select(-c(x)), data.frame(res_diff3) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[3], "with no adverse events")
})
test_that("with drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
if (!"RELATED" %in% res_ae$AEREL) {
res_ae <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEREL = "RELATED", n = 0)
}
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae4 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop4 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff4 <- res_prop4 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 4) |> dplyr::select(-c("x$name", x)), res_ae4 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 4) |> dplyr::select(-c(x)), data.frame(res_prop4) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 4) |> dplyr::select(-c(x)), data.frame(res_diff4) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[4], "with drug-related{^a} adverse events")
})
test_that("non-serious adverse events count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y" & AESER != "Y" | AESER == "")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae5 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop5 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff5 <- res_prop5 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 5) |> dplyr::select(-c("x$name", x)), res_ae5 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 5) |> dplyr::select(-c(x)), data.frame(res_prop5) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 5) |> dplyr::select(-c(x)), data.frame(res_diff5) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[5], "with non-serious adverse events")
})
test_that("serious adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESER) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESER = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(AESER == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESER")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae6 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop6 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff6 <- res_prop6 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 6) |> dplyr::select(-c("x$name", x)), res_ae6 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 6) |> dplyr::select(-c(x)), data.frame(res_prop6) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 6) |> dplyr::select(-c(x)), data.frame(res_diff6) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[6], "with serious adverse events")
})
test_that("serious drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESER, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESER = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(AESER == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESER", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae7 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop7 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff7 <- res_prop7 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 7) |> dplyr::select(-c("x$name", x)), res_ae7 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 7) |> dplyr::select(-c(x)), data.frame(res_prop7) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 7) |> dplyr::select(-c(x)), data.frame(res_diff7) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[7], "with serious drug-related adverse events")
})
test_that("death count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESDTH) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESDTH = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(AESDTH == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESDTH")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae8 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop8 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff8 <- res_prop8 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 8) |> dplyr::select(-c("x$name", x)), res_ae8 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 8) |> dplyr::select(-c(x)), data.frame(res_prop8) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 8) |> dplyr::select(-c(x)), data.frame(res_diff8) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[8], "who died")
})
test_that("died due to drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AESDTH, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AESDTH = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(AESDTH == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AESDTH", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae9 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop9 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff9 <- res_prop9 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 9) |> dplyr::select(-c("x$name", x)), res_ae9 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 9) |> dplyr::select(-c(x)), data.frame(res_prop9) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 9) |> dplyr::select(-c(x)), data.frame(res_diff9) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[9], "who died due to a drug-related adverse event")
})
test_that("discontinued due to an adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae10 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop10 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff10 <- res_prop10 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 10) |> dplyr::select(-c("x$name", x)), res_ae10 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 10) |> dplyr::select(-c(x)), data.frame(res_prop10) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 10) |> dplyr::select(-c(x)), data.frame(res_diff10) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[10], "discontinued any drug due to an adverse events")
})
test_that("discontinued due to drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae11 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop11 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff11 <- res_prop11 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 11) |> dplyr::select(-c("x$name", x)), res_ae11 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 11) |> dplyr::select(-c(x)), data.frame(res_prop11) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 11) |> dplyr::select(-c(x)), data.frame(res_diff11) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[11], "discontinued any drug due to a drug-related adverse events")
})
test_that("discontinued due to serious adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AESER) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::ungroup() |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AESER = "Y", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AESER")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae12 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop12 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff12 <- res_prop12 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 12) |> dplyr::select(-c("x$name", x)), res_ae12 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 12) |> dplyr::select(-c(x)), data.frame(res_prop12) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 12) |> dplyr::select(-c(x)), data.frame(res_diff12) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[12], "discontinued any drug due to a serious adverse event")
})
test_that("discontinued due to serious drug-related adverse event count", {
adae_itt <- dplyr::full_join(
adsl |> dplyr::select(USUBJID, TRT01AN, ITTFL), # Merge with adsl to get percentage
adae,
by = "USUBJID",
multiple = "all"
) |> subset(ITTFL == "Y" & TRTEMFL == "Y")
adae_tot <- adae_itt |> dplyr::mutate(TRT01AN = 99) # Add total rows into calculation
adae_tot <- rbind(adae_itt, adae_tot)
res_ae <- adae_tot |>
dplyr::group_by(TRT01AN, ITTFL, AEACN, AESER, AEREL) |>
dplyr::summarize(n = dplyr::n_distinct(USUBJID), .groups = "drop")
res_ae_example <- res_ae |>
dplyr::select(TRT01AN, ITTFL) |>
dplyr::distinct() |>
dplyr::mutate(AEACN = "DRUG WITHDRAWN", AESER = "Y", AEREL = "RELATED", t = as.integer(0))
res_ae <- res_ae |> subset(toupper(AEACN) == "DRUG WITHDRAWN" & AESER == "Y" & AEREL == "RELATED")
res_ae <- dplyr::left_join(res_ae_example,
res_ae,
by = c("TRT01AN", "ITTFL", "AEACN", "AESER", "AEREL")
) |> dplyr::select(-c(t))
res_ae[is.na(res_ae)] <- 0
res_ae <- data.frame(
dplyr::full_join(
res_ae, res_tot1,
by = c("TRT01AN", "ITTFL"),
multiple = "all"
)
) |>
dplyr::mutate(pct = formatC(100 * n / z, digits = 13, format = "f", flag = "0"))
res_ae$pct <- as.numeric(res_ae$pct)
res_ae <- res_ae |>
dplyr::mutate(
x = paste0("n_", dplyr::row_number()),
prop = paste0("prop_", dplyr::row_number())
)
res_ae13 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "x",
values_from = n,
values_fill = list(n = 0)
)
res_prop13 <- res_ae |>
tidyr::pivot_wider(
id_cols = "ITTFL",
names_from = "prop",
values_from = pct,
values_fill = list(pct = 0)
)
res_diff13 <- res_prop13 |> dplyr::mutate(
diff_2 = as.numeric(prop_2) - as.numeric(prop_1),
diff_3 = as.numeric(prop_3) - as.numeric(prop_1)
)
# n count
expect_equal(xx |> dplyr::filter(x == 13) |> dplyr::select(-c("x$name", x)), res_ae13 |> dplyr::select(-c(ITTFL)))
# Proportion count
expect_equal(xxp |> dplyr::filter(x == 13) |> dplyr::select(-c(x)), data.frame(res_prop13) |> dplyr::select(-c(ITTFL)))
# Diff count
expect_equal(xxpd |> dplyr::filter(x == 13) |> dplyr::select(-c(x)), data.frame(res_diff13) |> dplyr::select(c(diff_2, diff_3)))
# Name matches
expect_equal(x$name[13], "discontinued any drug due to a serious drug-related adverse event")
})
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