junco (version 0.1.2)

a_summarize_ex_j: Tabulation for Exposure Tables

Description

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
)

Value

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

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, comparison between groups will be performed.
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

(numeric)
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.

Functions

• s_summarize_ex_j(): Statistics function needed for the exposure tables.

• a_summarize_ex_j(): Formatted analysis function which is used as afun.

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.

Examples

library(dplyr)
ADEX <- ex_adsl |> select(USUBJID, ARM, TRTSDTM, EOSSTT, EOSDY)

trtvar <- "ARM"
ctrl_grp <- "B: Placebo"
cutoffd <- as.Date("2023-09-24")

ADEX <- ADEX |>
  create_colspan_var(
    non_active_grp          = ctrl_grp,
    non_active_grp_span_lbl = " ",
    active_grp_span_lbl     = "Active Study Agent",
    colspan_var             = "colspan_trt",
    trt_var                 = trtvar
  ) |>
  mutate(
    diff_header = "Difference in Means (95% CI)",
    diff_label = paste(!!rlang::sym(trtvar), "vs", ctrl_grp),
    TRTDURY = case_when(
      !is.na(EOSDY) ~ EOSDY,
      TRUE ~ as.integer(cutoffd - as.Date(TRTSDTM) + 1)
    )
  )

colspan_trt_map <- create_colspan_map(ADEX,
  non_active_grp = ctrl_grp,
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = trtvar
)

ref_path <- c("colspan_trt", "", trtvar, ctrl_grp)

lyt <- basic_table() |>
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  ) |>
  split_cols_by(trtvar) |>
  split_cols_by("diff_header", nested = FALSE) |>
  split_cols_by(
    trtvar,
    split_fun = remove_split_levels(ctrl_grp),
    labels_var = "diff_label"
  ) |>
  analyze("EOSDY",
    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 = trtvar, covariates = NULL),
      ancova = TRUE,
      comp_btw_group = TRUE
    )
  )

result <- build_table(lyt, ADEX, alt_counts_df = ADEX)
result