Tabulation for Exposure Tables — a_summarize_ex

A function to create the appropriate statistics needed for exposure table

Usage

s_summarize_ex_j(
  df,
  .var,
  .df_row,
  .spl_context,
  comp_btw_group = TRUE,
  ref_path = NULL,
  ancova = FALSE,
  interaction_y,
  interaction_item,
  conf_level,
  daysconv,
  variables
)

a_summarize_ex_j(
  df,
  .var,
  .df_row,
  .spl_context,
  comp_btw_group = TRUE,
  ref_path = NULL,
  ancova = FALSE,
  interaction_y = FALSE,
  interaction_item = NULL,
  conf_level = 0.95,
  variables,
  .stats = c("mean_sd", "median", "range", "quantiles", "total_subject_years"),
  .formats = c(diff_mean_est_ci = jjcsformat_xx("xx.xx (xx.xx, xx.xx)")),
  .labels = c(quantiles = "Interquartile range"),
  .indent_mods = NULL,
  na_str = rep("NA", 3),
  daysconv = 1
)

Arguments

df

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

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

.df_row

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

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

comp_btw_group

(logical)
If TRUE,
When ancova = FALSE, the estimate of between group difference (on CHG) will be based upon two-sample t-test.

When ancova = TRUE, the same ancova model will be used for the estimate of between group difference (on CHG).

ref_path

(character)
global reference group specification, see get_ref_info().

ancova

(logical)
If FALSE, only descriptive methods will be used.
If TRUE Ancova methods will be used for each of the columns : AVAL, CHG, DIFF.

interaction_y

(character)
Will be passed onto the tern function s_ancova, when ancova = TRUE.

interaction_item

(character)
Will be passed onto the tern function s_ancova, when ancova = TRUE.

conf_level

(proportion)
Confidence level of the interval

daysconv

conversion required to get the values into days (i.e 1 if original PARAMCD unit is days, 30.4375 if original PARAMCD unit is in months)

variables

(named list of strings)
list of additional analysis variables, with expected elements:

arm (string)
group variable, for which the covariate adjusted means of multiple groups will be summarized. Specifically, the first level of arm variable is taken as the reference group.
covariates (character)
a vector that can contain single variable names (such as 'X1'), and/or interaction terms indicated by 'X1 * X2'.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the 'auto' setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

na_str

(string)
string used to replace all NA or empty values in the output.

Value

a_summarize_ex_j() returns the corresponding list with formatted rtables::CellValue().

Details

Creates statistics needed for standard exposure table This includes differences and 95% CI and total treatment years. This is designed to be used as an analysis (afun in analyze) function.

Creates statistics needed for table. This includes differences and 95% CI and total treatment years. This is designed to be used as an analysis (afun in analyze) function.

Functions

s_summarize_ex_j(): Statistics function needed for the exposure tables
a_summarize_ex_j(): Formatted analysis function which is used as afun.

Examples

library(dplyr)

ADEX <- data.frame(
  USUBJID = c(
    "XXXXX01", "XXXXX02", "XXXXX03", "XXXXX04", "XXXXX05",
    "XXXXX06", "XXXXX07", "XXXXX08", "XXXXX09", "XXXXX10"
  ),
  TRT01A = c(
    "ARMA", "ARMA", "ARMA", "ARMA", "ARMA",
    "Placebo", "Placebo", "Placebo", "ARMA", "ARMA"
  ),
  AVAL = c(56, 78, 67, 87, 88, 93, 39, 87, 65, 55)
)

ADEX <- ADEX |>
  mutate(TRT01A = as.factor(TRT01A))

ADEX$colspan_trt <- factor(ifelse(ADEX$TRT01A == "Placebo", " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)

ADEX$diff_header <- "Difference in Means (95% CI)"
ADEX$diff_label <- paste(ADEX$TRT01A, paste("vs", "Placebo"))

colspan_trt_map <- create_colspan_map(ADEX,
  non_active_grp = "Placebo",
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = "TRT01A"
)
ref_path <- c("colspan_trt", "", "TRT01A", "Placebo")

lyt <- basic_table() |>
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  ) |>
  split_cols_by("TRT01A") |>
  split_cols_by("diff_header", nested = FALSE) |>
  split_cols_by(
    "TRT01A",
    split_fun = remove_split_levels("Placebo"),
    labels_var = "diff_label"
  ) |>
  analyze("AVAL",
    afun = a_summarize_ex_j, var_labels = "Duration of treatment (Days)",
    show_labels = "visible",
    indent_mod = 0L,
    extra_args = list(
      daysconv = 1,
      ref_path = ref_path,
      variables = list(arm = "TRT01A", covariates = NULL),
      ancova = TRUE,
      comp_btw_group = TRUE
    )
  )

result <- build_table(lyt, ADEX)

result
#>                                     Active Study Agent                    Difference in Means (95% CI)
#>                                            ARMA             Placebo             ARMA vs Placebo       
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————
#> Duration of treatment (Days)                                                                          
#>   Mean (SD)                           70.86 (13.704)     73.00 (29.597)      -2.14 (-32.33, 28.04)    
#>   Median                                  67.00              87.00                                    
#>   Min, max                             55.00, 88.00       39.00, 93.00                                
#>   Interquartile range                  56.00, 87.00       39.00, 93.00                                
#>   Total treatment (subject years)      496.0 (1.4)        219.0 (0.6)