# Comparing Against Baselines or Control

## 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)
Loading required package: magrittr
basic_table() %>%
split_cols_by("ARM") %>%
analyze("AGE") %>%
build_table(DM)
       A: Drug X   B: Placebo   C: Combination
----------------------------------------------
Mean     34.91       33.02          34.57     

and then the difference of the average AGE between the arms to the placebo arm:

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")
)
}) %>%
build_table(DM)
                         B: Placebo   A: Drug X   C: Combination
----------------------------------------------------------------
Difference of Averages       0          1.89           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:

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"))
}) %>%
build_table(DM)
                         B: Placebo   A: Drug X   C: Combination
----------------------------------------------------------------
Difference of Averages                  1.89           1.55     
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)
)
}) %>%
build_table(DM)
                         B: Placebo   A: Drug X   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 fro analyze.

## Row Splitting

When adding row-splitting the reference data might be one represented by the column with or without row splitting. For example:

basic_table() %>%
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))
)
}) %>%
build_table(subset(DM, SEX %in% c("M", "F")))
                               B: Placebo   A: Drug X   C: Combination
(N=106)      (N=121)       (N=129)
----------------------------------------------------------------------
F
is reference (.in_ref_col)      TRUE        FALSE         FALSE
ref cell N (.ref_group)          56          56             56
ref column N (.ref_full)        106          106           106
M
is reference (.in_ref_col)      TRUE        FALSE         FALSE
ref cell N (.ref_group)          50          50             50
ref column N (.ref_full)        106          106           106      

so the data assigned to .ref_full is the full data of the reference column where 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.