junco (version 0.1.4)

a_two_tier: Two Tier Analysis Function

Description

The analysis function used as an afun in analyze. This function simulates a final additional level of nesting with a traditional analyze call inside it.

This makes it possible to create what appear to be group summary or content rows and to optionally or conditionally generate one or more "detail" rows underneath it.

For example, in a disposition table, one might want counts for completed and ongoing patients with no further detail underneath, but a breakdown of specific reasons beneath the count of patients who discontinued treatment.

Usage

a_two_tier(
  df,
  labelstr = NULL,
  .var,
  .N_col,
  .df_row,
  inner_var,
  drill_down_levs,
  .spl_context,
  use_all_levels = FALSE,
  grp_fun,
  detail_fun,
  .alt_df_full = NULL,
  ...
)

Value

A RowsVerticalSection object including both the group row and all detail rows, if applicable, for the facet.

Arguments

df

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

labelstr

(character)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

.var

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

.N_col

(integer)
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

.df_row

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

inner_var

(string)
single variable name to use when generating the detail rows.

drill_down_levs

(character)
the level(s) of .var under which detail rows should be generated.

.spl_context

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

use_all_levels

(flag)
controls which factor levels will be present for inner_var (both in df/x and in .df_row) when calling detail_fun. If TRUE, all levels (those present on the factor .df_row[[inner_var]], *regardless if the level is observed in the row group or not) will be present when creating detail rows. Otherwise (the default), only the levels observed anywhere in the row group, i.e., within .df_row will be present.

grp_fun

(function)
analysis function to be used when generating the "group summary" outer rows.

detail_fun

(function)
analysis function to be used when generating "detail" inner rows.

.alt_df_full

(dataframe)
denominator dataset for fraction and relative risk calculations.
this argument gets populated by the rtables split machinery (see rtables::additional_fun_params).

...

additional arguments passed directly to grp_fun and detail_fun.

Author

GB, WW.

Details

Both the analysis variable and inner_var must be factors. Detail rows are differentiated by having an indent mod of one, causing them to hang indented under their corresponding group row.

Examples

# Example 1

lyt_obs_levels <- basic_table() |>
  split_cols_by("ARM") |>
  split_rows_by("EOSSTT", child_labels = "hidden") |>
  analyze("EOSSTT",
    afun = a_two_tier,
    extra_args = list(
      grp_fun = simple_analysis,
      detail_fun = simple_analysis,
      inner_var = "DCSREAS",
      drill_down_levs = "DISCONTINUED"
    )
  )

tbl <- build_table(lyt_obs_levels, ex_adsl)
tbl

lyt_all_levels <- basic_table() |>
  split_cols_by("ARM") |>
  split_rows_by("EOSSTT", child_labels = "hidden") |>
  analyze("EOSSTT",
    afun = a_two_tier,
    extra_args = list(
      grp_fun = simple_analysis,
      detail_fun = simple_analysis,
      inner_var = "DCSREAS",
      drill_down_levs = "DISCONTINUED",
      use_all_levels = TRUE
    )
  )

adsl_subset <- subset(ex_adsl, DCSREAS != "ADVERSE EVENT")
levels(adsl_subset$DCSREAS)

tbl_all_levels <- build_table(lyt_all_levels, adsl_subset)
tbl_all_levels

tbl_obs_levels <- build_table(lyt_obs_levels, adsl_subset)
tbl_obs_levels

# Example 2

library(dplyr)

trtvar <- "ARM"
ctrl_grp <- "B: Placebo"

adsl <- ex_adsl |> select(c("USUBJID", "STRATA1", "EOSSTT", "DCSREAS", all_of(trtvar)))
adsl$colspan_trt <- factor(
  ifelse(adsl[[trtvar]] == ctrl_grp, " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)
adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))

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

a_freq_j_args <- list(
  .stats = "count_unique_fraction",
  denom = "n_altdf",
  ref_path = c("colspan_trt", " ", trtvar, ctrl_grp)
)

two_tier_args <- list(
  grp_fun = a_freq_j,
  detail_fun = a_freq_j,
  inner_var = "DCSREAS",
  drill_down_levs = "DISCONTINUED"
)

lyt_rrisk <- basic_table() |>
  split_cols_by("colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map)) |>
  split_cols_by(trtvar) |>
  split_cols_by("rrisk_header", nested = FALSE) |>
  split_cols_by(trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels(ctrl_grp)) |>
  split_rows_by("STRATA1") |>
  split_rows_by("EOSSTT", child_labels = "hidden") |>
  analyze("EOSSTT", afun = a_two_tier, extra_args = c(two_tier_args, a_freq_j_args))

adsl_subset <- subset(
  adsl,
  EOSSTT != "COMPLETED" & (is.na(DCSREAS) | DCSREAS != "PROTOCOL VIOLATION")
)

tbl_rrisk <- build_table(lyt_rrisk, adsl_subset, alt_counts_df = adsl_subset)
tbl_rrisk