Analysis Function for Response Variables

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 = list(strata = NULL),
  conf_level = formals(s_proportion_diff)$conf_level,
  method = c("wald", "waldcc", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc", "cmh_sato", "cmh_mn", "uncond_exact_diff"),
  weights_method = formals(s_proportion_diff)$weights_method,
  .formats = NULL,
  na_str = rep("NA", 3),
  legacy = FALSE
)

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.

.formats

(character or NULL)
formats to apply to the statistics. If NULL, default formats will be used.

na_str

(character)
string to use for NA values. Defaults to rep("NA", 3).

legacy

(logical(1))
To allow for backward compatibility default of used format.

Value

Formatted analysis function which is used as afun in analyze_vars() and as cfun in summarize_row_groups().

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
#>                        A: Drug X        B: Placebo     C: Combination
#>                         (N=134)          (N=134)          (N=132)    
#> —————————————————————————————————————————————————————————————————————
#> SEX                                                                  
#>   F                  72/72 (100.0%)   73/73 (100.0%)   61/61 (100.0%)
#>   M                  46/46 (100.0%)   48/48 (100.0%)   53/53 (100.0%)
#>   U                   3/3 (100.0%)     2/2 (100.0%)     4/4 (100.0%) 
#>   UNDIFFERENTIATED    1/1 (100.0%)          -           2/2 (100.0%)