View source: R/summarize_change.R
summarize_change | R Documentation |
The primary analysis variable .var
indicates the numerical change from baseline results,
and additional required secondary analysis variables are value
and baseline_flag
.
Depending on the baseline flag, either the absolute baseline values (at baseline)
or the change from baseline values (post-baseline) are then summarized.
summarize_change(
lyt,
vars,
variables,
na_str = default_na_str(),
nested = TRUE,
...,
table_names = vars,
.stats = c("n", "mean_sd", "median", "range"),
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
s_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)
a_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)
lyt |
( |
vars |
( |
variables |
(named |
na_str |
( |
nested |
( |
... |
additional arguments for the lower level functions. |
table_names |
( |
.stats |
( |
.formats |
(named |
.labels |
(named |
.indent_mods |
(named |
df |
( |
.var |
( |
na.rm |
( |
summarize_change()
returns a layout object suitable for passing to further layouting functions,
or to rtables::build_table()
. Adding this function to an rtable
layout will add formatted rows containing
the statistics from s_change_from_baseline()
to the table layout.
s_change_from_baseline()
returns the same values returned by s_summary.numeric()
.
a_change_from_baseline()
returns the corresponding list with formatted rtables::CellValue()
.
summarize_change()
: Layout-creating function which can take statistics function arguments
and additional format arguments. This function is a wrapper for rtables::analyze()
.
s_change_from_baseline()
: Statistics function that summarizes baseline or post-baseline visits.
a_change_from_baseline()
: Formatted analysis function which is used as afun
in summarize_change()
.
To be used after a split on visits in the layout, such that each data subset only contains either baseline or post-baseline data.
The data in df
must be either all be from baseline or post-baseline visits. Otherwise
an error will be thrown.
library(dplyr)
## Fabricate dataset
dta_test <- data.frame(
USUBJID = rep(1:6, each = 3),
AVISIT = rep(paste0("V", 1:3), 6),
ARM = rep(LETTERS[1:3], rep(6, 3)),
AVAL = c(9:1, rep(NA, 9))
) %>%
mutate(ABLFLL = AVISIT == "V1") %>%
group_by(USUBJID) %>%
mutate(
BLVAL = AVAL[ABLFLL],
CHG = AVAL - BLVAL
) %>%
ungroup()
results <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL")) %>%
build_table(dta_test)
results
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