Tabulation for Exposure Tables

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 This is designed to be used as an analysis (afun in `analyze`) function.

Creates statistics needed for table. This includes differences and 95 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)