In this vignette we create a
using the rtables layout facility. That is, we
demonstrate how the layout based tabulation framework can specify the
structure and relations that are commonly found when analyzing clinical
trials data.
Note that all the data is created using random number generators. All
ex_* data which is currently attached to the
rtables package is provided by the formatters
package and was created using the publicly available random.cdisc.data
R package.
The packages used in this vignette are:
Demographic tables summarize the variables content for different population subsets (encoded in the columns).
One feature of analyze() that we have not introduced in
the previous vignette is that the analysis function afun
can specify multiple rows with the in_rows() function:
ADSL <- ex_adsl # Example ADSL dataset
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(vars = "AGE", afun = function(x) {
in_rows(
"Mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"Range" = rcell(range(x), format = "xx.xx - xx.xx")
)
})
tbl <- build_table(lyt, ADSL)
tbl
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————————
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00Multiple variables can be analyzed in one analyze()
call:
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(vars = c("AGE", "BMRKR1"), afun = function(x) {
in_rows(
"Mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"Range" = rcell(range(x), format = "xx.xx - xx.xx")
)
})
tbl2 <- build_table(lyt2, ADSL)
tbl2
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————
# AGE
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR1
# Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49)
# Range 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39Hence, if afun can process different data vector types
(i.e. variables selected from the data) then we are fairly close to a
standard demographic table. Here is a function that either creates a
count table or some number summary if the argument x is a
factor or numeric, respectively:
s_summary <- function(x) {
if (is.numeric(x)) {
in_rows(
"n" = rcell(sum(!is.na(x)), format = "xx"),
"Mean (sd)" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = "xx.xx (xx.xx)"),
"IQR" = rcell(IQR(x, na.rm = TRUE), format = "xx.xx"),
"min - max" = rcell(range(x, na.rm = TRUE), format = "xx.xx - xx.xx")
)
} else if (is.factor(x)) {
vs <- as.list(table(x))
do.call(in_rows, lapply(vs, rcell, format = "xx"))
} else {
stop("type not supported")
}
}Note we use rcell to wrap the results in order to add
formatting instructions for rtables. We can use
s_summary outside the context of tabulation:
s_summary(ADSL$AGE)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod row_label
# 1 n 400 0 n
# 2 Mean (sd) 34.88 (7.44) 0 Mean (sd)
# 3 IQR 10.00 0 IQR
# 4 min - max 20.00 - 69.00 0 min - maxand
s_summary(ADSL$SEX)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod row_label
# 1 F 222 0 F
# 2 M 166 0 M
# 3 U 9 0 U
# 4 UNDIFFERENTIATED 3 0 UNDIFFERENTIATEDWe can now create a commonly used variant of the demographic table:
summary_lyt <- basic_table() %>%
split_cols_by(var = "ARM") %>%
analyze(c("AGE", "SEX"), afun = s_summary)
summary_tbl <- build_table(summary_lyt, ADSL)
summary_tbl
# A: Drug X B: Placebo C: Combination
# ———————————————————————————————————————————————————————————————————
# AGE
# n 134 134 132
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# IQR 11.00 10.00 10.00
# min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2Note that analyze() can also be called multiple times in
sequence:
summary_lyt2 <- basic_table() %>%
split_cols_by(var = "ARM") %>%
analyze("AGE", s_summary) %>%
analyze("SEX", s_summary)
summary_tbl2 <- build_table(summary_lyt2, ADSL)
summary_tbl2
# A: Drug X B: Placebo C: Combination
# ———————————————————————————————————————————————————————————————————
# AGE
# n 134 134 132
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# IQR 11.00 10.00 10.00
# min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2which leads to the table identical to summary_tbl:
In clinical trials analyses the number of patients per column is
often referred to as N (rather than the overall population
which outside of clinical trials is commonly referred to as
N). Column Ns are added by setting the
show_colcounts argument in basic_table() to
TRUE:
summary_lyt3 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARMCD") %>%
analyze(c("AGE", "SEX"), s_summary)
summary_tbl3 <- build_table(summary_lyt3, ADSL)
summary_tbl3
# ARM A ARM B ARM C
# (N=134) (N=134) (N=132)
# ——————————————————————————————————————————————————————————————————
# AGE
# n 134 134 132
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# IQR 11.00 10.00 10.00
# min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2We will now show a couple of variations of the demographic table that
we developed above. These variations are in structure and not in
analysis, hence they don’t require a modification to the
s_summary function.
We will start with a standard table analyzing the variables
AGE and BMRKR2 variables:
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
analyze(c("AGE", "BMRKR2"), s_summary)
tbl <- build_table(lyt, ADSL)
tbl
# A: Drug X B: Placebo C: Combination
# (N=134) (N=134) (N=132)
# ————————————————————————————————————————————————————————————
# AGE
# n 134 134 132
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# IQR 11.00 10.00 10.00
# min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 50 45 40
# MEDIUM 37 56 42
# HIGH 47 33 50Assume we would like to have this analysis carried out per gender encoded in the row space:
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX") %>%
analyze(c("AGE", "BMRKR2"), s_summary)
tbl <- build_table(lyt, ADSL)
tbl
# A: Drug X B: Placebo C: Combination
# (N=134) (N=134) (N=132)
# —————————————————————————————————————————————————————————————————
# F
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# BMRKR2
# LOW 26 21 26
# MEDIUM 21 38 17
# HIGH 32 18 23
# M
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 21 23 11
# MEDIUM 15 18 23
# HIGH 15 14 26
# U
# AGE
# n 3 2 4
# Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10)
# IQR 3.00 4.00 3.25
# min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00
# BMRKR2
# LOW 2 1 1
# MEDIUM 1 0 2
# HIGH 0 1 1
# UNDIFFERENTIATED
# AGE
# n 1 0 2
# Mean (sd) 28.00 (NA) NA 45.00 (1.41)
# IQR 0.00 NA 1.00
# min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00
# BMRKR2
# LOW 1 0 2
# MEDIUM 0 0 0
# HIGH 0 0 0We will now subset ADSL to include only males and
females in the analysis in order to reduce the number of rows in the
table:
ADSL_M_F <- filter(ADSL, SEX %in% c("M", "F"))
lyt2 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX") %>%
analyze(c("AGE", "BMRKR2"), s_summary)
tbl2 <- build_table(lyt2, ADSL_M_F)
tbl2
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# —————————————————————————————————————————————————————————————————
# F
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# BMRKR2
# LOW 26 21 26
# MEDIUM 21 38 17
# HIGH 32 18 23
# M
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 21 23 11
# MEDIUM 15 18 23
# HIGH 15 14 26
# U
# AGE
# n 0 0 0
# Mean (sd) NA NA NA
# IQR NA NA NA
# min - max Inf - -Inf Inf - -Inf Inf - -Inf
# BMRKR2
# LOW 0 0 0
# MEDIUM 0 0 0
# HIGH 0 0 0
# UNDIFFERENTIATED
# AGE
# n 0 0 0
# Mean (sd) NA NA NA
# IQR NA NA NA
# min - max Inf - -Inf Inf - -Inf Inf - -Inf
# BMRKR2
# LOW 0 0 0
# MEDIUM 0 0 0
# HIGH 0 0 0Note that the UNDIFFERENTIATED and U levels
still show up in the table. This is because tabulation respects the
factor levels and level order, exactly as the split and
table function do. If empty levels should be dropped then
rtables needs to know that at splitting time via the
split_fun argument in split_rows_by(). There
are a number of predefined functions. For this example
drop_split_levels() is required to drop the empty levels at
splitting time. Splitting is a big topic and will be eventually
addressed in a specific package vignette.
lyt3 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels, child_labels = "visible") %>%
analyze(c("AGE", "BMRKR2"), s_summary)
tbl3 <- build_table(lyt3, ADSL_M_F)
tbl3
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ——————————————————————————————————————————————————————————————
# F
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# BMRKR2
# LOW 26 21 26
# MEDIUM 21 38 17
# HIGH 32 18 23
# M
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 21 23 11
# MEDIUM 15 18 23
# HIGH 15 14 26In the table above the labels M and F are
not very descriptive. You can add the full labels as follows:
ADSL_M_F_l <- ADSL_M_F %>%
mutate(lbl_sex = case_when(
SEX == "M" ~ "Male",
SEX == "F" ~ "Female",
SEX == "U" ~ "Unknown",
SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
))
lyt4 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
analyze(c("AGE", "BMRKR2"), s_summary)
tbl4 <- build_table(lyt4, ADSL_M_F_l)
tbl4
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ——————————————————————————————————————————————————————————————
# Female
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# BMRKR2
# LOW 26 21 26
# MEDIUM 21 38 17
# HIGH 32 18 23
# Male
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 21 23 11
# MEDIUM 15 18 23
# HIGH 15 14 26For the next table variation we only stratify by gender for the
AGE analysis. To do this the nested argument
has to be set to FALSE in analyze() call:
lyt5 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
analyze("AGE", s_summary, show_labels = "visible") %>%
analyze("BMRKR2", s_summary, nested = FALSE, show_labels = "visible")
tbl5 <- build_table(lyt5, ADSL_M_F_l)
tbl5
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ——————————————————————————————————————————————————————————————
# Female
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# Male
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 47 44 37
# MEDIUM 36 56 40
# HIGH 47 32 49Once we split the rows into groups (Male and
Female here) one might want to summarize groups: usually by
showing count and column percentages. This is especially important if we
have missing data. For example, if we create the above table but add
missing data to the AGE variable:
insert_NAs <- function(x) {
x[sample(c(TRUE, FALSE), length(x), TRUE, prob = c(0.2, 0.8))] <- NA
x
}
set.seed(1)
ADSL_NA <- ADSL_M_F_l %>%
mutate(AGE = insert_NAs(AGE))
lyt6 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by(
"SEX",
labels_var = "lbl_sex",
split_fun = drop_split_levels,
child_labels = "visible"
) %>%
analyze("AGE", s_summary) %>%
analyze("BMRKR2", s_summary, nested = FALSE, show_labels = "visible")
tbl6 <- build_table(lyt6, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl6
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ————————————————————————————————————————————————————————————
# Female
# n 65 61 54
# Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78)
# IQR 9.00 10.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00
# Male
# n 44 44 50
# Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42)
# IQR 10.50 8.25 10.75
# min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00
# BMRKR2
# LOW 47 44 37
# MEDIUM 36 56 40
# HIGH 47 32 49Here it is not easy to see how many females and males there are in
each arm as n represents the number of non-missing data
elements in the variables. Groups within rows that are defined by
splitting can be summarized with summarize_row_groups(),
for example:
lyt7 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
summarize_row_groups() %>%
analyze("AGE", s_summary) %>%
analyze("BMRKR2", afun = s_summary, nested = FALSE, show_labels = "visible")
tbl7 <- build_table(lyt7, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl7
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ————————————————————————————————————————————————————————————
# Female 79 (60.8%) 77 (58.3%) 66 (52.4%)
# n 65 61 54
# Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78)
# IQR 9.00 10.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00
# Male 51 (39.2%) 55 (41.7%) 60 (47.6%)
# n 44 44 50
# Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42)
# IQR 10.50 8.25 10.75
# min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00
# BMRKR2
# LOW 47 44 37
# MEDIUM 36 56 40
# HIGH 47 32 49There are a couple of things to note here:
summarize_row_groups()).We can recreate this default behavior (count percentage) by defining
a cfun for illustrative purposes here as it results in the
same table as above:
lyt8 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
in_rows(
rcell(nrow(df) * c(1, 1 / .N_col), format = "xx (xx.xx%)"),
.labels = labelstr
)
}) %>%
analyze("AGE", s_summary) %>%
analyze("BEP01FL", afun = s_summary, nested = FALSE, show_labels = "visible")
tbl8 <- build_table(lyt8, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl8
# A: Drug X B: Placebo C: Combination
# (N=130) (N=132) (N=126)
# ————————————————————————————————————————————————————————————
# Female 79 (60.77%) 77 (58.33%) 66 (52.38%)
# n 65 61 54
# Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78)
# IQR 9.00 10.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00
# Male 51 (39.23%) 55 (41.67%) 60 (47.62%)
# n 44 44 50
# Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42)
# IQR 10.50 8.25 10.75
# min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00
# BEP01FL
# Y 67 63 65
# N 63 69 61Note that cfun, like afun (which is used in
analyze()), can operate on either variables, passed via the
x argument, or data.frames or
tibbles, which are passed via the df argument
(afun can optionally request df too). Unlike
afun, cfun must accept labelstr
as the second argument which gives the default group label (factor level
from splitting) and hence it could be modified:
lyt9 <- basic_table() %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "hidden") %>%
summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
in_rows(
rcell(nrow(df) * c(1, 1 / .N_col), format = "xx (xx.xx%)"),
.labels = paste0(labelstr, ": count (perc.)")
)
}) %>%
analyze("AGE", s_summary) %>%
analyze("BEP01FL", s_summary, nested = FALSE, show_labels = "visible")
tbl9 <- build_table(lyt9, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl9
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————————————————————
# Female: count (perc.) 79 (60.77%) 77 (58.33%) 66 (52.38%)
# n 65 61 54
# Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78)
# IQR 9.00 10.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00
# Male: count (perc.) 51 (39.23%) 55 (41.67%) 60 (47.62%)
# n 44 44 50
# Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42)
# IQR 10.50 8.25 10.75
# min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00
# BEP01FL
# Y 67 63 65
# N 63 69 61Layouts have a couple of advantages over tabulating the tables directly:
Here is an example that demonstrates the reusability of layouts:
adsl_lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze(c("AGE", "SEX"), afun = s_summary)
adsl_lyt
# A Pre-data Table Layout
#
# Column-Split Structure:
# ARM (lvls)
#
# Row-Split Structure:
# AGE:SEX (** multivar analysis **)We can now build a table for ADSL
adsl_tbl <- build_table(adsl_lyt, ADSL)
adsl_tbl
# A: Drug X B: Placebo C: Combination
# (N=134) (N=134) (N=132)
# ———————————————————————————————————————————————————————————————————
# AGE
# n 134 134 132
# Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72)
# IQR 11.00 10.00 10.00
# min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# SEX
# F 79 77 66
# M 51 55 60
# U 3 2 4
# UNDIFFERENTIATED 1 0 2or for all patients that are older than 18:
adsl_f_tbl <- build_table(lyt, ADSL %>% filter(AGE > 18))
# Warning in min(x): no non-missing arguments to min; returning Inf
# Warning in max(x): no non-missing arguments to max; returning -Inf
adsl_f_tbl
# A: Drug X B: Placebo C: Combination
# (N=134) (N=134) (N=132)
# —————————————————————————————————————————————————————————————————
# F
# AGE
# n 79 77 66
# Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43)
# IQR 9.00 8.00 6.75
# min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00
# BMRKR2
# LOW 26 21 26
# MEDIUM 21 38 17
# HIGH 32 18 23
# M
# AGE
# n 51 55 60
# Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24)
# IQR 11.00 9.00 11.00
# min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00
# BMRKR2
# LOW 21 23 11
# MEDIUM 15 18 23
# HIGH 15 14 26
# U
# AGE
# n 3 2 4
# Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10)
# IQR 3.00 4.00 3.25
# min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00
# BMRKR2
# LOW 2 1 1
# MEDIUM 1 0 2
# HIGH 0 1 1
# UNDIFFERENTIATED
# AGE
# n 1 0 2
# Mean (sd) 28.00 (NA) NA 45.00 (1.41)
# IQR 0.00 NA 1.00
# min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00
# BMRKR2
# LOW 1 0 2
# MEDIUM 0 0 0
# HIGH 0 0 0There are a number of different adverse event tables. We will now present two tables that show adverse events by ID and then by grade and by ID.
This time we won’t use the ADAE dataset from random.cdisc.data
but rather generate a dataset on the fly (see Adrian’s
2016 Phuse paper):
set.seed(1)
lookup <- tribble(
~AEDECOD, ~AEBODSYS, ~AETOXGR,
"HEADACHE", "NERVOUS SYSTEM DISORDERS", "5",
"BACK PAIN", "MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS", "2",
"GINGIVAL BLEEDING", "GASTROINTESTINAL DISORDERS", "1",
"HYPOTENSION", "VASCULAR DISORDERS", "3",
"FAECES SOFT", "GASTROINTESTINAL DISORDERS", "2",
"ABDOMINAL DISCOMFORT", "GASTROINTESTINAL DISORDERS", "1",
"DIARRHEA", "GASTROINTESTINAL DISORDERS", "1",
"ABDOMINAL FULLNESS DUE TO GAS", "GASTROINTESTINAL DISORDERS", "1",
"NAUSEA (INTERMITTENT)", "GASTROINTESTINAL DISORDERS", "2",
"WEAKNESS", "MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS", "3",
"ORTHOSTATIC HYPOTENSION", "VASCULAR DISORDERS", "4"
)
normalize <- function(x) x / sum(x)
weightsA <- normalize(c(0.1, dlnorm(seq(0, 5, length.out = 25), meanlog = 3)))
weightsB <- normalize(c(0.2, dlnorm(seq(0, 5, length.out = 25))))
N_pop <- 300
ADSL2 <- data.frame(
USUBJID = seq(1, N_pop, by = 1),
ARM = sample(c("ARM A", "ARM B"), N_pop, TRUE),
SEX = sample(c("F", "M"), N_pop, TRUE),
AGE = 20 + rbinom(N_pop, size = 40, prob = 0.7)
)
l.adae <- mapply(
ADSL2$USUBJID,
ADSL2$ARM,
ADSL2$SEX,
ADSL2$AGE,
FUN = function(id, arm, sex, age) {
n_ae <- sample(0:25, 1, prob = if (arm == "ARM A") weightsA else weightsB)
i <- sample(seq_len(nrow(lookup)), size = n_ae, replace = TRUE, prob = c(6, rep(1, 10)) / 16)
lookup[i, ] %>%
mutate(
AESEQ = seq_len(n()),
USUBJID = id, ARM = arm, SEX = sex, AGE = age
)
},
SIMPLIFY = FALSE
)
ADAE2 <- do.call(rbind, l.adae)
ADAE2 <- ADAE2 %>%
mutate(
ARM = factor(ARM, levels = c("ARM A", "ARM B")),
AEDECOD = as.factor(AEDECOD),
AEBODSYS = as.factor(AEBODSYS),
AETOXGR = factor(AETOXGR, levels = as.character(1:5))
) %>%
select(USUBJID, ARM, AGE, SEX, AESEQ, AEDECOD, AEBODSYS, AETOXGR)
ADAE2
# # A tibble: 3,118 × 8
# USUBJID ARM AGE SEX AESEQ AEDECOD AEBODSYS AETOXGR
# <dbl> <fct> <dbl> <chr> <int> <fct> <fct> <fct>
# 1 1 ARM A 45 F 1 NAUSEA (INTERMITTENT) GASTROINTESTIN… 2
# 2 1 ARM A 45 F 2 HEADACHE NERVOUS SYSTEM… 5
# 3 1 ARM A 45 F 3 HEADACHE NERVOUS SYSTEM… 5
# 4 1 ARM A 45 F 4 HEADACHE NERVOUS SYSTEM… 5
# 5 1 ARM A 45 F 5 HEADACHE NERVOUS SYSTEM… 5
# 6 1 ARM A 45 F 6 HEADACHE NERVOUS SYSTEM… 5
# 7 1 ARM A 45 F 7 HEADACHE NERVOUS SYSTEM… 5
# 8 1 ARM A 45 F 8 HEADACHE NERVOUS SYSTEM… 5
# 9 1 ARM A 45 F 9 HEADACHE NERVOUS SYSTEM… 5
# 10 1 ARM A 45 F 10 FAECES SOFT GASTROINTESTIN… 2
# # ℹ 3,108 more rowsWe start by defining an events summary function:
s_events_patients <- function(x, labelstr, .N_col) {
in_rows(
"Total number of patients with at least one event" =
rcell(length(unique(x)) * c(1, 1 / .N_col), format = "xx (xx.xx%)"),
"Total number of events" = rcell(length(x), format = "xx")
)
}So, for a population of 5 patients where
AEsAEAEswe would get the following summary:
s_events_patients(x = c("id 1", "id 1", "id 2"), .N_col = 5)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod
# 1 Total number of patients with at least one event 2 (40.00%) 0
# 2 Total number of events 3 0
# row_label
# 1 Total number of patients with at least one event
# 2 Total number of eventsThe .N_col argument is a special keyword argument by
which build_table() passes the population size for each
respective column. For a list of keyword arguments for the functions
passed to afun in analyze(), refer to the
documentation with ?analyze.
We now use the s_events_patients summary function in a
tabulation:
adae_lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze("USUBJID", s_events_patients)
adae_tbl <- build_table(adae_lyt, ADAE2)
adae_tbl
# ARM A ARM B
# (N=2060) (N=1058)
# —————————————————————————————————————————————————————————————————————————————
# Total number of patients with at least one event 114 (5.53%) 150 (14.18%)
# Total number of events 2060 1058Note that the column Ns are wrong as by default they are
set to the number of rows per group (i.e. number of AEs per
arm here). This also affects the percentages. For this table we are
interested in the number of patients per column/arm which is usually
taken from ADSL (var ADSL2 here).
rtables handles this by allowing us to override how the
column counts are computed. We can specify an alt_counts_df
in build_table(). When we do this, rtables
calculates the column counts by applying the same column faceting to
alt_counts_df as it does to the primary data during
tabulation:
adae_adsl_tbl <- build_table(adae_lyt, ADAE2, alt_counts_df = ADSL2)
adae_adsl_tbl
# ARM A ARM B
# (N=146) (N=154)
# ——————————————————————————————————————————————————————————————————————————————
# Total number of patients with at least one event 114 (78.08%) 150 (97.40%)
# Total number of events 2060 1058Alternatively, if the desired column counts are already calculated,
they can be specified directly via the col_counts argument
to build_table(), though specifying an
alt_counts_df is the preferred mechanism (the number of
rows will be used, but no duplicate checking!!!).
We next calculate this information per system organ class:
adae_soc_lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze("USUBJID", s_events_patients) %>%
split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE) %>%
summarize_row_groups("USUBJID", cfun = s_events_patients)
adae_soc_tbl <- build_table(adae_soc_lyt, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl
# ARM A ARM B
# (N=146) (N=154)
# ————————————————————————————————————————————————————————————————————————————————
# Total number of patients with at least one event 114 (78.08%) 150 (97.40%)
# Total number of events 2060 1058
# GASTROINTESTINAL DISORDERS
# Total number of patients with at least one event 114 (78.08%) 130 (84.42%)
# Total number of events 760 374
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# Total number of patients with at least one event 98 (67.12%) 81 (52.60%)
# Total number of events 273 142
# NERVOUS SYSTEM DISORDERS
# Total number of patients with at least one event 113 (77.40%) 133 (86.36%)
# Total number of events 787 420
# VASCULAR DISORDERS
# Total number of patients with at least one event 93 (63.70%) 75 (48.70%)
# Total number of events 240 122We now have to add a count table of AEDECOD for each
AEBODSYS. The default analyze() behavior for a
factor is to create the count table per level (using
rtab_inner):
adae_soc_lyt2 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
analyze("AEDECOD", indent_mod = -1)
adae_soc_tbl2 <- build_table(adae_soc_lyt2, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl2
# ARM A ARM B
# (N=146) (N=154)
# ——————————————————————————————————————————————————————————————————————————————————
# GASTROINTESTINAL DISORDERS
# Total number of patients with at least one event 114 (78.08%) 130 (84.42%)
# Total number of events 760 374
# ABDOMINAL DISCOMFORT 113 65
# ABDOMINAL FULLNESS DUE TO GAS 119 65
# BACK PAIN 0 0
# DIARRHEA 107 53
# FAECES SOFT 122 58
# GINGIVAL BLEEDING 147 71
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 152 62
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# Total number of patients with at least one event 98 (67.12%) 81 (52.60%)
# Total number of events 273 142
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 135 75
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 138 67
# NERVOUS SYSTEM DISORDERS
# Total number of patients with at least one event 113 (77.40%) 133 (86.36%)
# Total number of events 787 420
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 787 420
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
# VASCULAR DISORDERS
# Total number of patients with at least one event 93 (63.70%) 75 (48.70%)
# Total number of events 240 122
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 104 58
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 136 64
# WEAKNESS 0 0The indent_mod argument enables relative indenting
changes if the tree structure of the table does not result in the
desired indentation by default.
This table so far is however not the usual adverse event table as it counts the total number of events and not the number of subjects for one or more events for a particular term. To get the correct table we need to write a custom analysis function:
table_count_once_per_id <- function(df, termvar = "AEDECOD", idvar = "USUBJID") {
x <- df[[termvar]]
id <- df[[idvar]]
counts <- table(x[!duplicated(id)])
in_rows(
.list = as.vector(counts),
.labels = names(counts)
)
}
table_count_once_per_id(ADAE2)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod
# 1 ABDOMINAL DISCOMFORT 23 0
# 2 ABDOMINAL FULLNESS DUE TO GAS 21 0
# 3 BACK PAIN 20 0
# 4 DIARRHEA 7 0
# 5 FAECES SOFT 11 0
# 6 GINGIVAL BLEEDING 15 0
# 7 HEADACHE 100 0
# 8 HYPOTENSION 16 0
# 9 NAUSEA (INTERMITTENT) 21 0
# 10 ORTHOSTATIC HYPOTENSION 14 0
# 11 WEAKNESS 16 0
# row_label
# 1 ABDOMINAL DISCOMFORT
# 2 ABDOMINAL FULLNESS DUE TO GAS
# 3 BACK PAIN
# 4 DIARRHEA
# 5 FAECES SOFT
# 6 GINGIVAL BLEEDING
# 7 HEADACHE
# 8 HYPOTENSION
# 9 NAUSEA (INTERMITTENT)
# 10 ORTHOSTATIC HYPOTENSION
# 11 WEAKNESSSo the desired AE table is:
adae_soc_lyt3 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
analyze("AEDECOD", afun = table_count_once_per_id, show_labels = "hidden", indent_mod = -1)
adae_soc_tbl3 <- build_table(adae_soc_lyt3, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl3
# ARM A ARM B
# (N=146) (N=154)
# ——————————————————————————————————————————————————————————————————————————————————
# GASTROINTESTINAL DISORDERS
# Total number of patients with at least one event 114 (78.08%) 130 (84.42%)
# Total number of events 760 374
# ABDOMINAL DISCOMFORT 24 28
# ABDOMINAL FULLNESS DUE TO GAS 18 26
# BACK PAIN 0 0
# DIARRHEA 17 17
# FAECES SOFT 17 14
# GINGIVAL BLEEDING 18 25
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 20 20
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# Total number of patients with at least one event 98 (67.12%) 81 (52.60%)
# Total number of events 273 142
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 58 45
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 40 36
# NERVOUS SYSTEM DISORDERS
# Total number of patients with at least one event 113 (77.40%) 133 (86.36%)
# Total number of events 787 420
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 113 133
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
# VASCULAR DISORDERS
# Total number of patients with at least one event 93 (63.70%) 75 (48.70%)
# Total number of events 240 122
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 44 31
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 49 44
# WEAKNESS 0 0Note that we are missing the overall summary in the first two rows.
This can be added with an initial analyze() call.
adae_soc_lyt4 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze("USUBJID", afun = s_events_patients) %>%
split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1, section_div = "") %>%
summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
analyze("AEDECOD", table_count_once_per_id, show_labels = "hidden", indent_mod = -1)
adae_soc_tbl4 <- build_table(adae_soc_lyt4, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl4
# ARM A ARM B
# (N=146) (N=154)
# ——————————————————————————————————————————————————————————————————————————————————
# Total number of patients with at least one event 114 (78.08%) 150 (97.40%)
# Total number of events 2060 1058
# GASTROINTESTINAL DISORDERS
# Total number of patients with at least one event 114 (78.08%) 130 (84.42%)
# Total number of events 760 374
# ABDOMINAL DISCOMFORT 24 28
# ABDOMINAL FULLNESS DUE TO GAS 18 26
# BACK PAIN 0 0
# DIARRHEA 17 17
# FAECES SOFT 17 14
# GINGIVAL BLEEDING 18 25
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 20 20
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
#
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# Total number of patients with at least one event 98 (67.12%) 81 (52.60%)
# Total number of events 273 142
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 58 45
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 40 36
#
# NERVOUS SYSTEM DISORDERS
# Total number of patients with at least one event 113 (77.40%) 133 (86.36%)
# Total number of events 787 420
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 113 133
# HYPOTENSION 0 0
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 0 0
# WEAKNESS 0 0
#
# VASCULAR DISORDERS
# Total number of patients with at least one event 93 (63.70%) 75 (48.70%)
# Total number of events 240 122
# ABDOMINAL DISCOMFORT 0 0
# ABDOMINAL FULLNESS DUE TO GAS 0 0
# BACK PAIN 0 0
# DIARRHEA 0 0
# FAECES SOFT 0 0
# GINGIVAL BLEEDING 0 0
# HEADACHE 0 0
# HYPOTENSION 44 31
# NAUSEA (INTERMITTENT) 0 0
# ORTHOSTATIC HYPOTENSION 49 44
# WEAKNESS 0 0Finally, if we wanted to prune the 0 count rows we can do that with
the trim_rows() function:
trim_rows(adae_soc_tbl4)
# ARM A ARM B
# (N=146) (N=154)
# ——————————————————————————————————————————————————————————————————————————————————
# Total number of patients with at least one event 114 (78.08%) 150 (97.40%)
# Total number of events 2060 1058
# GASTROINTESTINAL DISORDERS
# Total number of patients with at least one event 114 (78.08%) 130 (84.42%)
# Total number of events 760 374
# ABDOMINAL DISCOMFORT 24 28
# ABDOMINAL FULLNESS DUE TO GAS 18 26
# DIARRHEA 17 17
# FAECES SOFT 17 14
# GINGIVAL BLEEDING 18 25
# NAUSEA (INTERMITTENT) 20 20
#
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# Total number of patients with at least one event 98 (67.12%) 81 (52.60%)
# Total number of events 273 142
# BACK PAIN 58 45
# WEAKNESS 40 36
#
# NERVOUS SYSTEM DISORDERS
# Total number of patients with at least one event 113 (77.40%) 133 (86.36%)
# Total number of events 787 420
# HEADACHE 113 133
#
# VASCULAR DISORDERS
# Total number of patients with at least one event 93 (63.70%) 75 (48.70%)
# Total number of events 240 122
# HYPOTENSION 44 31
# ORTHOSTATIC HYPOTENSION 49 44Pruning is a larger topic with a separate
rtables package vignette.
The adverse events table by ID and by grade shows how many patients had at least one adverse event per grade for different subsets of the data (e.g. defined by system organ class).
For this table we do not show the zero count grades. Note that we add the “overall” groups with a custom split function.
table_count_grade_once_per_id <- function(df,
labelstr = "",
gradevar = "AETOXGR",
idvar = "USUBJID",
grade_levels = NULL) {
id <- df[[idvar]]
grade <- df[[gradevar]]
if (!is.null(grade_levels)) {
stopifnot(all(grade %in% grade_levels))
grade <- factor(grade, levels = grade_levels)
}
id_sel <- !duplicated(id)
in_rows(
"--Any Grade--" = sum(id_sel),
.list = as.list(table(grade[id_sel]))
)
}
table_count_grade_once_per_id(ex_adae, grade_levels = 1:5)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod row_label
# 1 --Any Grade-- 365 0 --Any Grade--
# 2 1 131 0 1
# 3 2 70 0 2
# 4 3 74 0 3
# 5 4 25 0 4
# 6 5 65 0 5All of the layouting concepts needed to create this table have already been introduced so far:
adae_grade_lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze(
"AETOXGR",
afun = table_count_grade_once_per_id,
extra_args = list(grade_levels = 1:5),
var_labels = "- Any adverse events -",
show_labels = "visible"
) %>%
split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
summarize_row_groups(cfun = table_count_grade_once_per_id, format = "xx", indent_mod = 1) %>%
split_rows_by("AEDECOD", child_labels = "visible", indent_mod = -2) %>%
analyze(
"AETOXGR",
afun = table_count_grade_once_per_id,
extra_args = list(grade_levels = 1:5),
show_labels = "hidden"
)
adae_grade_tbl <- build_table(adae_grade_lyt, ADAE2, alt_counts_df = ADSL2)
adae_grade_tbl
# ARM A ARM B
# (N=146) (N=154)
# —————————————————————————————————————————————————————————————————————
# - Any adverse events -
# --Any Grade-- 114 150
# 1 32 34
# 2 22 30
# 3 11 21
# 4 8 6
# 5 41 59
# GASTROINTESTINAL DISORDERS
# --Any Grade-- 114 130
# 1 77 96
# 2 37 34
# 3 0 0
# 4 0 0
# 5 0 0
# ABDOMINAL DISCOMFORT
# --Any Grade-- 68 49
# 1 68 49
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ABDOMINAL FULLNESS DUE TO GAS
# --Any Grade-- 73 51
# 1 73 51
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# BACK PAIN
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# DIARRHEA
# --Any Grade-- 68 40
# 1 68 40
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# FAECES SOFT
# --Any Grade-- 76 44
# 1 0 0
# 2 76 44
# 3 0 0
# 4 0 0
# 5 0 0
# GINGIVAL BLEEDING
# --Any Grade-- 80 52
# 1 80 52
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HEADACHE
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# NAUSEA (INTERMITTENT)
# --Any Grade-- 83 50
# 1 0 0
# 2 83 50
# 3 0 0
# 4 0 0
# 5 0 0
# ORTHOSTATIC HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# WEAKNESS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
# --Any Grade-- 98 81
# 1 0 0
# 2 58 45
# 3 40 36
# 4 0 0
# 5 0 0
# ABDOMINAL DISCOMFORT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ABDOMINAL FULLNESS DUE TO GAS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# BACK PAIN
# --Any Grade-- 79 62
# 1 0 0
# 2 79 62
# 3 0 0
# 4 0 0
# 5 0 0
# DIARRHEA
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# FAECES SOFT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# GINGIVAL BLEEDING
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HEADACHE
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# NAUSEA (INTERMITTENT)
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ORTHOSTATIC HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# WEAKNESS
# --Any Grade-- 73 43
# 1 0 0
# 2 0 0
# 3 73 43
# 4 0 0
# 5 0 0
# NERVOUS SYSTEM DISORDERS
# --Any Grade-- 113 133
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 113 133
# ABDOMINAL DISCOMFORT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ABDOMINAL FULLNESS DUE TO GAS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# BACK PAIN
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# DIARRHEA
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# FAECES SOFT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# GINGIVAL BLEEDING
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HEADACHE
# --Any Grade-- 113 133
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 113 133
# HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# NAUSEA (INTERMITTENT)
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ORTHOSTATIC HYPOTENSION
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# WEAKNESS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# VASCULAR DISORDERS
# --Any Grade-- 93 75
# 1 0 0
# 2 0 0
# 3 44 31
# 4 49 44
# 5 0 0
# ABDOMINAL DISCOMFORT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ABDOMINAL FULLNESS DUE TO GAS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# BACK PAIN
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# DIARRHEA
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# FAECES SOFT
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# GINGIVAL BLEEDING
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HEADACHE
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# HYPOTENSION
# --Any Grade-- 66 43
# 1 0 0
# 2 0 0
# 3 66 43
# 4 0 0
# 5 0 0
# NAUSEA (INTERMITTENT)
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0
# ORTHOSTATIC HYPOTENSION
# --Any Grade-- 70 54
# 1 0 0
# 2 0 0
# 3 0 0
# 4 70 54
# 5 0 0
# WEAKNESS
# --Any Grade-- 0 0
# 1 0 0
# 2 0 0
# 3 0 0
# 4 0 0
# 5 0 0The response table that we will create here is composed of 3 parts:
Let’s start with the first part which is fairly simple to derive:
ADRS_BESRSPI <- ex_adrs %>%
filter(PARAMCD == "BESRSPI") %>%
mutate(
rsp = factor(AVALC %in% c("CR", "PR"), levels = c(TRUE, FALSE), labels = c("Responders", "Non-Responders")),
is_rsp = (rsp == "Responders")
)
s_proportion <- function(x, .N_col) {
in_rows(
.list = lapply(
as.list(table(x)),
function(xi) rcell(xi * c(1, 1 / .N_col), format = "xx.xx (xx.xx%)")
)
)
}
rsp_lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARMCD", ref_group = "ARM A") %>%
analyze("rsp", s_proportion, show_labels = "hidden")
rsp_tbl <- build_table(rsp_lyt, ADRS_BESRSPI)
rsp_tbl
# ARM A ARM B ARM C
# (N=134) (N=134) (N=132)
# ———————————————————————————————————————————————————————————————————
# Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%)
# Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%)Note that we did set the ref_group argument in
split_cols_by() which for the current table had no effect
as we only use the cell data for the responder and non-responder counts.
The ref_group argument is needed for the part 2 and 3 of
the table.
We will now look the implementation of part 2: unstratified analysis comparison vs. control group. Let’s start with the analysis function:
s_unstrat_resp <- function(x, .ref_group, .in_ref_col) {
if (.in_ref_col) {
return(in_rows(
"Difference in Response Rates (%)" = rcell(numeric(0)),
"95% CI (Wald, with correction)" = rcell(numeric(0)),
"p-value (Chi-Squared Test)" = rcell(numeric(0)),
"Odds Ratio (95% CI)" = rcell(numeric(0))
))
}
fit <- stats::prop.test(
x = c(sum(x), sum(.ref_group)),
n = c(length(x), length(.ref_group)),
correct = FALSE
)
fit_glm <- stats::glm(
formula = rsp ~ group,
data = data.frame(
rsp = c(.ref_group, x),
group = factor(rep(c("ref", "x"), times = c(length(.ref_group), length(x))), levels = c("ref", "x"))
),
family = binomial(link = "logit")
)
in_rows(
"Difference in Response Rates (%)" = non_ref_rcell(
(mean(x) - mean(.ref_group)) * 100,
.in_ref_col,
format = "xx.xx"
),
"95% CI (Wald, with correction)" = non_ref_rcell(
fit$conf.int * 100,
.in_ref_col,
format = "(xx.xx, xx.xx)"
),
"p-value (Chi-Squared Test)" = non_ref_rcell(
fit$p.value,
.in_ref_col,
format = "x.xxxx | (<0.0001)"
),
"Odds Ratio (95% CI)" = non_ref_rcell(
c(
exp(stats::coef(fit_glm)[-1]),
exp(stats::confint.default(fit_glm, level = .95)[-1, , drop = FALSE])
),
.in_ref_col,
format = "xx.xx (xx.xx - xx.xx)"
)
)
}
s_unstrat_resp(
x = ADRS_BESRSPI %>% filter(ARM == "A: Drug X") %>% pull(is_rsp),
.ref_group = ADRS_BESRSPI %>% filter(ARM == "B: Placebo") %>% pull(is_rsp),
.in_ref_col = FALSE
)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod
# 1 Difference in Response Rates (%) 17.91 0
# 2 95% CI (Wald, with correction) (7.93, 27.89) 0
# 3 p-value (Chi-Squared Test) 0.0006 0
# 4 Odds Ratio (95% CI) 2.79 (1.53 - 5.06) 0
# row_label
# 1 Difference in Response Rates (%)
# 2 95% CI (Wald, with correction)
# 3 p-value (Chi-Squared Test)
# 4 Odds Ratio (95% CI)Hence we can now add the next vignette to the table:
rsp_lyt2 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARMCD", ref_group = "ARM A") %>%
analyze("rsp", s_proportion, show_labels = "hidden") %>%
analyze(
"is_rsp", s_unstrat_resp,
show_labels = "visible",
var_labels = "Unstratified Response Analysis"
)
rsp_tbl2 <- build_table(rsp_lyt2, ADRS_BESRSPI)
rsp_tbl2
# ARM A ARM B ARM C
# (N=134) (N=134) (N=132)
# ——————————————————————————————————————————————————————————————————————————————————————————————
# Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%)
# Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%)
# Unstratified Response Analysis
# Difference in Response Rates (%) -17.91 5.83
# 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61)
# p-value (Chi-Squared Test) 0.0006 0.1436
# Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75)Next we will add part 3: the multinomial response table. To do so, we are adding a row-split by response level, and then doing the same thing as we did for the binary response table above.
s_prop <- function(df, .N_col) {
in_rows(
"95% CI (Wald, with correction)" = rcell(binom.test(nrow(df), .N_col)$conf.int * 100, format = "(xx.xx, xx.xx)")
)
}
s_prop(
df = ADRS_BESRSPI %>% filter(ARM == "A: Drug X", AVALC == "CR"),
.N_col = sum(ADRS_BESRSPI$ARM == "A: Drug X")
)
# RowsVerticalSection (in_rows) object print method:
# ----------------------------
# row_name formatted_cell indent_mod
# 1 95% CI (Wald, with correction) (49.38, 66.67) 0
# row_label
# 1 95% CI (Wald, with correction)We can now create the final response table with all three parts:
rsp_lyt3 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARMCD", ref_group = "ARM A") %>%
analyze("rsp", s_proportion, show_labels = "hidden") %>%
analyze(
"is_rsp", s_unstrat_resp,
show_labels = "visible", var_labels = "Unstratified Response Analysis"
) %>%
split_rows_by(
var = "AVALC",
split_fun = reorder_split_levels(neworder = c("CR", "PR", "SD", "PD", "NE"), drlevels = TRUE),
nested = FALSE
) %>%
summarize_row_groups() %>%
analyze("AVALC", afun = s_prop)
rsp_tbl3 <- build_table(rsp_lyt3, ADRS_BESRSPI)
rsp_tbl3
# ARM A ARM B ARM C
# (N=134) (N=134) (N=132)
# ——————————————————————————————————————————————————————————————————————————————————————————————
# Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%)
# Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%)
# Unstratified Response Analysis
# Difference in Response Rates (%) -17.91 5.83
# 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61)
# p-value (Chi-Squared Test) 0.0006 0.1436
# Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75)
# CR 78 (58.2%) 55 (41.0%) 97 (73.5%)
# 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79)
# PR 36 (26.9%) 35 (26.1%) 23 (17.4%)
# 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99)
# SD 20 (14.9%) 44 (32.8%) 12 (9.1%)
# 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34)
# PD 0 (0.0%) 0 (0.0%) 0 (0.0%)
# 95% CI (Wald, with correction) (0.00, 2.72) (0.00, 2.72) (0.00, 2.76)
# NE 0 (0.0%) 0 (0.0%) 0 (0.0%)
# 95% CI (Wald, with correction) (0.00, 2.72) (0.00, 2.72) (0.00, 2.76)In the case that we wanted to rename the levels of AVALC
and remove the CI for NE we could do that as follows:
rsp_label <- function(x) {
rsp_full_label <- c(
CR = "Complete Response (CR)",
PR = "Partial Response (PR)",
SD = "Stable Disease (SD)",
`NON CR/PD` = "Non-CR or Non-PD (NON CR/PD)",
PD = "Progressive Disease (PD)",
NE = "Not Evaluable (NE)",
Missing = "Missing",
`NE/Missing` = "Missing or unevaluable"
)
stopifnot(all(x %in% names(rsp_full_label)))
rsp_full_label[x]
}
rsp_lyt4 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARMCD", ref_group = "ARM A") %>%
analyze("rsp", s_proportion, show_labels = "hidden") %>%
analyze(
"is_rsp", s_unstrat_resp,
show_labels = "visible", var_labels = "Unstratified Response Analysis"
) %>%
split_rows_by(
var = "AVALC",
split_fun = keep_split_levels(c("CR", "PR", "SD", "PD"), reorder = TRUE),
nested = FALSE
) %>%
summarize_row_groups(cfun = function(df, labelstr, .N_col) {
in_rows(nrow(df) * c(1, 1 / .N_col), .formats = "xx (xx.xx%)", .labels = rsp_label(labelstr))
}) %>%
analyze("AVALC", afun = s_prop) %>%
analyze("AVALC", afun = function(x, .N_col) {
in_rows(rcell(sum(x == "NE") * c(1, 1 / .N_col), format = "xx.xx (xx.xx%)"), .labels = rsp_label("NE"))
}, nested = FALSE)
rsp_tbl4 <- build_table(rsp_lyt4, ADRS_BESRSPI)
rsp_tbl4
# ARM A ARM B ARM C
# (N=134) (N=134) (N=132)
# ——————————————————————————————————————————————————————————————————————————————————————————————
# Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%)
# Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%)
# Unstratified Response Analysis
# Difference in Response Rates (%) -17.91 5.83
# 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61)
# p-value (Chi-Squared Test) 0.0006 0.1436
# Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75)
# Complete Response (CR) 78 (58.21%) 55 (41.04%) 97 (73.48%)
# 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79)
# Partial Response (PR) 36 (26.87%) 35 (26.12%) 23 (17.42%)
# 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99)
# Stable Disease (SD) 20 (14.93%) 44 (32.84%) 12 (9.09%)
# 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34)
# Progressive Disease (PD) 0 (0.00%) 0 (0.00%) 0 (0.00%)
# 95% CI (Wald, with correction) (0.00, 2.72) (0.00, 2.72) (0.00, 2.76)
# Not Evaluable (NE) 0.00 (0.00%) 0.00 (0.00%) 0.00 (0.00%)Note that the table is missing the rows gaps to make it more
readable. The row spacing feature is on the rtables roadmap
and will be implemented in future.
The time to event analysis table that will be constructed consists of four parts:
The table is constructed by sequential use of the
analyze() function, with four custom analysis functions
corresponding to each of the four parts listed above. In addition the
table includes referential footnotes relevant to the table contents. The
table will be faceted column-wise by arm.
First we will start by loading the necessary packages and preparing the data to be used in the construction of this table.
library(survival)
adtte <- ex_adaette %>%
dplyr::filter(PARAMCD == "AETTE2", SAFFL == "Y")
# Add censoring to data for example
adtte[adtte$AVAL > 1.0, ] <- adtte[adtte$AVAL > 1.0, ] %>% mutate(AVAL = 1.0, CNSR = 1)
adtte2 <- adtte %>%
mutate(CNSDTDSC = ifelse(CNSDTDSC == "", "__none__", CNSDTDSC))The adtte dataset will be used in preparing the models
while the adtte2 dataset handles missing values in the
“Censor Date Description” column and will be used to produce the final
table. We add censoring into the data for example purposes.
Next we create a basic analysis function, a_count_subjs
which prints the overall unique subject counts and percentages within
the data.
a_count_subjs <- function(x, .N_col) {
in_rows(
"Subjects with Adverse Events n (%)" = rcell(length(unique(x)) * c(1, 1 / .N_col), format = "xx (xx.xx%)")
)
}Then an analysis function is created to generate the counts of
censored subjects for each level of a factor variable in the dataset. In
this case the cnsr_counter function will be applied with
the CNSDTDSC variable which contains a censor date
description for each censored subject.
cnsr_counter <- function(df, .var, .N_col) {
x <- df[!duplicated(df$USUBJID), .var]
x <- x[x != "__none__"]
lapply(table(x), function(xi) rcell(xi * c(1, 1 / .N_col), format = "xx (xx.xx%)"))
}This function generates counts and fractions of unique subjects corresponding to each factor level, excluding missing values (uncensored patients).
A Cox proportional-hazards (Cox P-H) analysis is generated next with
a third custom analysis function, a_cph. Prior to creating
the analysis function, the Cox P-H model is fit to our data using the
coxph() and Surv() functions from the
survival package. Then this model is used as input to the
a_cph analysis function which returns hazard ratios, 95%
confidence intervals, and p-values comparing against the reference group
- in this case the leftmost column.
cph <- coxph(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, ties = "exact", data = adtte)
a_cph <- function(df, .var, .in_ref_col, .ref_full, full_cox_fit) {
if (.in_ref_col) {
ret <- replicate(3, list(rcell(NULL)))
} else {
curtrt <- df[[.var]][1]
coefs <- coef(full_cox_fit)
sel_pos <- grep(curtrt, names(coefs), fixed = TRUE)
hrval <- exp(coefs[sel_pos])
sdf <- survdiff(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, data = rbind(df, .ref_full))
pval <- (1 - pchisq(sdf$chisq, length(sdf$n) - 1)) / 2
ci_val <- exp(unlist(confint(full_cox_fit)[sel_pos, ]))
ret <- list(
rcell(hrval, format = "xx.x"),
rcell(ci_val, format = "(xx.x, xx.x)"),
rcell(pval, format = "x.xxxx | (<0.0001)")
)
}
in_rows(
.list = ret,
.names = c("Hazard ratio", "95% confidence interval", "p-value (one-sided stratified log rank)")
)
}The fourth and final analysis function, a_tte, generates
a time to first adverse event table with three rows corresponding to
Median, 95% Confidence Interval, and Min Max respectively. First a
survival table is constructed from the summary table of a survival model
using the survfit() and Surv() functions from
the survival package. This table is then given as input to
a_tte which produces the table of time to first adverse
event consisting of the previously mentioned summary statistics.
surv_tbl <- as.data.frame(
summary(survfit(Surv(AVAL, CNSR == 0) ~ ACTARM, data = adtte, conf.type = "log-log"))$table
) %>%
dplyr::mutate(
ACTARM = factor(gsub("ACTARM=", "", row.names(.)), levels = levels(adtte$ACTARM)),
ind = FALSE
)
a_tte <- function(df, .var, kp_table) {
ind <- grep(df[[.var]][1], row.names(kp_table), fixed = TRUE)
minmax <- range(df[["AVAL"]])
mm_val_str <- format_value(minmax, format = "xx.x, xx.x")
rowfn <- list()
if (all(df$CNSR[df$AVAL == minmax[2]])) {
mm_val_str <- paste0(mm_val_str, "*")
rowfn <- "* indicates censoring"
}
in_rows(
Median = kp_table[ind, "median", drop = TRUE],
"95% confidence interval" = unlist(kp_table[ind, c("0.95LCL", "0.95UCL")]),
"Min Max" = mm_val_str,
.formats = c("xx.xx", "xx.xx - xx.xx", "xx"),
.row_footnotes = list(NULL, NULL, rowfn)
)
}Additionally, the a_tte function creates a referential
footnote within the table to indicate where censoring occurred in the
data.
Now we are able to use these four analysis functions to build our time to event analysis table.
lyt <- basic_table(show_colcounts = TRUE) %>%
## Column faceting
split_cols_by("ARM", ref_group = "A: Drug X") %>%
## Overall count
analyze("USUBJID", a_count_subjs, show_labels = "hidden") %>%
## Censored subjects summary
analyze("CNSDTDSC", cnsr_counter, var_labels = "Censored Subjects", show_labels = "visible") %>%
## Cox P-H analysis
analyze("ARM", a_cph, extra_args = list(full_cox_fit = cph), show_labels = "hidden") %>%
## Time-to-event analysis
analyze(
"ARM", a_tte,
var_labels = "Time to first adverse event", show_labels = "visible",
extra_args = list(kp_table = surv_tbl),
table_names = "kapmeier"
)
tbl_tte <- build_table(lyt, adtte2)We set the show_colcounts argument of
basic_table() to TRUE to first print the total
subject counts for each column. Next we use split_cols_by()
to split the table into three columns corresponding to the three
different levels of ARM, and specify that the first arm,
"A: Drug X" should act as the reference group to be
compared against - this reference group is used for the Cox P-H
analysis. Then we call analyze() sequentially using each of
the four custom analysis functions as argument afun and
specifying additional arguments where necessary. Then we use
build_table() to construct our rtable using
the adtte2 dataset.
Finally, we annotate the table using the
fnotes_at_path() function to specify that product-limit
estimates are used to calculate the statistics listed under the “Time to
first adverse event” heading within the table. The referential footnote
created earlier in the time-to-event analysis function
(a_tte) is also displayed.
fnotes_at_path(
tbl_tte,
c("ma_USUBJID_CNSDTDSC_ARM_kapmeier", "kapmeier")
) <- "Product-limit (Kaplan-Meier) estimates."
tbl_tte
# A: Drug X B: Placebo C: Combination
# (N=134) (N=134) (N=132)
# ————————————————————————————————————————————————————————————————————————————————————————
# Subjects with Adverse Events n (%) 134 (100.00%) 134 (100.00%) 132 (100.00%)
# Censored Subjects
# Clinical Cut Off 6 (4.48%) 3 (2.24%) 14 (10.61%)
# Completion or Discontinuation 9 (6.72%) 5 (3.73%) 9 (6.82%)
# End of AE Reporting Period 14 (10.45%) 7 (5.22%) 14 (10.61%)
# Preferred Term 11 (8.21%) 5 (3.73%) 13 (9.85%)
# Hazard ratio 0.7 1.0
# 95% confidence interval (0.5, 0.9) (0.8, 1.4)
# p-value (one-sided stratified log rank) 0.1070 0.4880
# Time to first adverse event {1}
# Median 0.23 0.39 0.29
# 95% confidence interval 0.18 - 0.33 0.29 - 0.49 0.22 - 0.35
# Min Max {2} 0.0, 1.0* 0.0, 1.0* 0.0, 1.0*
# ————————————————————————————————————————————————————————————————————————————————————————
#
# {1} - Product-limit (Kaplan-Meier) estimates.
# {2} - * indicates censoring
# ————————————————————————————————————————————————————————————————————————————————————————