Comparing Against Baselines or Control
Gabriel Becker and Adrian Waddell
2024-12-05
Source:vignettes/baseline.Rmd
baseline.Rmd
Introduction
Often the data from one column is considered the reference/baseline/comparison group and is compared to the data from the other columns.
For example, lets calculate the average age:
library(rtables)
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze("AGE")
tbl <- build_table(lyt, DM)
tbl
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————
# Mean 34.91 33.02 34.57
and then the difference of the average AGE
between the
placebo arm and the other arms:
lyt2 <- basic_table() %>%
split_cols_by("ARM", ref_group = "B: Placebo") %>%
analyze("AGE", afun = function(x, .ref_group) {
in_rows(
"Difference of Averages" = rcell(mean(x) - mean(.ref_group), format = "xx.xx")
)
})
tbl2 <- build_table(lyt2, DM)
tbl2
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————
# Difference of Averages 1.89 0.00 1.55
Note that the column order has changed and the reference group is displayed in the first column.
In cases where we want cells to be blank in the reference column,
(e.g., “B: Placebo”) we use non_ref_rcell()
instead of
rcell()
, and pass .in_ref_col
as the second
argument:
lyt3 <- basic_table() %>%
split_cols_by("ARM", ref_group = "B: Placebo") %>%
analyze(
"AGE",
afun = function(x, .ref_group, .in_ref_col) {
in_rows(
"Difference of Averages" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = "xx.xx")
)
}
)
tbl3 <- build_table(lyt3, DM)
tbl3
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————
# Difference of Averages 1.89 1.55
lyt4 <- basic_table() %>%
split_cols_by("ARM", ref_group = "B: Placebo") %>%
analyze(
"AGE",
afun = function(x, .ref_group, .in_ref_col) {
in_rows(
"Difference of Averages" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = "xx.xx"),
"another row" = non_ref_rcell("aaa", .in_ref_col)
)
}
)
tbl4 <- build_table(lyt4, DM)
tbl4
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————
# Difference of Averages 1.89 1.55
# another row aaa aaa
You can see which arguments are available for afun
in
the manual for analyze()
.
Row Splitting
When adding row-splitting the reference data may be represented by the column with or without row splitting. For example:
lyt5 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM", ref_group = "B: Placebo") %>%
split_rows_by("SEX", split_fun = drop_split_levels) %>%
analyze("AGE", afun = function(x, .ref_group, .ref_full, .in_ref_col) {
in_rows(
"is reference (.in_ref_col)" = rcell(.in_ref_col),
"ref cell N (.ref_group)" = rcell(length(.ref_group)),
"ref column N (.ref_full)" = rcell(length(.ref_full))
)
})
tbl5 <- build_table(lyt5, subset(DM, SEX %in% c("M", "F")))
tbl5
# A: Drug X B: Placebo C: Combination
# (N=121) (N=106) (N=129)
# ——————————————————————————————————————————————————————————————————————
# F
# is reference (.in_ref_col) FALSE TRUE FALSE
# ref cell N (.ref_group) 56 56 56
# ref column N (.ref_full) 106 106 106
# M
# is reference (.in_ref_col) FALSE TRUE FALSE
# ref cell N (.ref_group) 50 50 50
# ref column N (.ref_full) 106 106 106
The data assigned to .ref_full
is the full data of the
reference column whereas the data assigned to .ref_group
respects the subsetting defined by row-splitting and hence is from the
same subset as the argument x
or df
to
afun
.