junco (version 0.1.4)

a_freq_resp_var_j: Analysis Function for Response Variables

Description

This function calculates counts and percentages for response variables (Y/N values), with optional risk difference calculations.

Usage

a_freq_resp_var_j(
  df,
  .var,
  .df_row,
  .N_col,
  .spl_context,
  resp_var = NULL,
  id = "USUBJID",
  drop_levels = FALSE,
  riskdiff = TRUE,
  ref_path = NULL,
  variables = formals(s_proportion_diff)$variables,
  conf_level = formals(s_proportion_diff)$conf_level,
  method = c("wald", "waldcc", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc"),
  weights_method = formals(s_proportion_diff)$weights_method,
  ...
)

Value

Formatted analysis function which is used as afun in analyze_vars()

and as cfun in summarize_row_groups().

Arguments

df

(data.frame)
data set containing all analysis variables.

.var

(string)
variable name that is passed by rtables.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

.N_col

(integer)
column-wise N (column count) for the full column being analyzed.

.spl_context

(data.frame)
gives information about ancestor split states.

resp_var

(string)
response variable name containing Y/N values.

id

(string)
subject variable name.

drop_levels

(logical)
if TRUE, non-observed levels will not be included.

riskdiff

(logical)
if TRUE, risk difference calculations will be performed.

ref_path

(string)
column path specifications for the control group.

variables

(list)
variables to include in the analysis.

conf_level

(proportion)
confidence level of the interval.

method

(character)
method for calculating confidence intervals.

weights_method

(character)
method for calculating weights.

...

Additional arguments passed to other functions.

Examples

library(dplyr)
ADSL <- ex_adsl |> select(USUBJID, ARM, SEX)

ADAE <- ex_adae |> select(USUBJID, ARM, SEX, AEBODSYS, AEDECOD)

ADAE <- ADAE |>
  mutate(TRTEMFL = "Y")

lyt <- basic_table(show_colcounts = TRUE) |>
  split_cols_by("ARM") |>
  analyze("SEX",
    show_labels = "visible",
    afun = a_freq_resp_var_j,
    extra_args = list(resp_var = "TRTEMFL", riskdiff = FALSE)
  )

result <- build_table(lyt, df = ADAE, alt_counts_df = ADSL)

result