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Analysis function count and percentage in column design controlled by combosdf

Source: R/a_freq_combos_j.R
a_freq_combos_j.Rd

Analysis function count and percentage in column design controlled by combosdf

Usage

a_freq_combos_j(
  df,
  labelstr = NULL,
  .var = NA,
  val = NULL,
  combosdf = NULL,
  do_not_filter = NULL,
  filter_var = NULL,
  flag_var = NULL,
  .df_row,
  .spl_context,
  .N_col,
  id = "USUBJID",
  denom = c("N_col", "n_df", "n_altdf", "n_rowdf", "n_parentdf"),
  label = NULL,
  label_fstr = NULL,
  label_map = NULL,
  .alt_df_full = NULL,
  denom_by = NULL,
  .stats = "count_unique_denom_fraction",
  .formats = NULL,
  .labels_n = NULL,
  .indent_mods = NULL,
  na_str = rep("NA", 3)
)

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.

val

(character or NULL)
When NULL, all levels of the incoming variable (variable used in the analyze call) will be considered.
When a single string, only that current level/value of the incoming variable will be considered.
When multiple levels, only those levels/values of the incoming variable will be considered.
When no values are observed (eg zero row input df), a row with row-label No data reported will be included in the table.

combosdf

The df which provides the mapping of column facets to produce cumulative counts for .N_col.
In the cell facet, these cumulative records must then be removed from the numerator, which can be done via the filter_var parameter to avoid unwanted counting of events.

do_not_filter

A vector of facets (i.e., column headers), identifying headers for which no filtering of records should occur. That is, the numerator should contain cumulative counts. Generally, this will be used for a "Total" column, or something similar.

filter_var

The variable which identifies the records to count in the numerator for any given column. Generally, this will contain text matching the column header for the column associated with a given record.

flag_var

Variable which identifies the occurrence (or first occurrence) of an event. The flag variable is expected to have a value of "Y" identifying that the event should be counted, or NA otherwise.

.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.

.N_col

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

id

(string)
subject variable name.

denom

(string)
One of

  • N_col Column count,

  • n_df Number of patients (based upon the main input dataframe df),

  • n_altdf Number of patients from the secondary dataframe (.alt_df_full),
    Note that argument denom_by will perform a row-split on the .alt_df_full dataframe.
    It is a requirement that variables specified in denom_by are part of the row split specifications.

  • n_rowdf Number of patients from the current row-level dataframe (.row_df from the rtables splitting machinery).

  • n_parentdf Number of patients from a higher row-level split than the current split.
    This higher row-level split is specified in the argument denom_by.

label

(string)
When val has length 1, the row label to be shown on the output can be specified using this argument.
When val is a character vector, the label_map argument can be specified to control the row-labels.

label_fstr

(string)
a sprintf style format string. It can contain up to one "%s", which takes the current split value and generates the row/column label.
It will be combined with the labelstr argument, when utilizing this function as a cfun in a summarize_row_groups call.
It is recommended not to utilize this argument for other purposes. The label argument could be used instead (if val is a single string)

label_map

(tibble)
A mapping tibble to translate levels from the incoming variable into a different row label to be presented on the table.

.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).

denom_by

(character)
Variables from row-split to be used in the denominator derivation.
This controls both denom = "n_parentdf" and denom = "n_altdf".
When denom = "n_altdf", the denominator is derived from .alt_df_full in combination with denom_by argument

.stats

(character)
statistics to select for the table.

.formats

(named 'character' or 'list')
formats for the statistics.

.labels_n

(named character)
String to control row labels for the 'n'-statistics.
Only useful when more than one 'n'-statistic is requested (rare situations only).

.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

list of requested statistics with formatted rtables::CellValue().

Examples

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

cutoffd <- as.Date("2023-09-24")

ADSL <- ADSL |>
  mutate(
    TRTDURY = case_when(
      !is.na(EOSDY) ~ EOSDY,
      TRUE ~ as.integer(cutoffd - as.Date(TRTSDTM) + 1)
    )
  ) |>
  mutate(ACAT1 = case_when(
    TRTDURY < 183 ~ "0-6 Months",
    TRTDURY < 366 ~ "6-12 Months",
    TRUE ~ "+12 Months"
  )) |>
  mutate(ACAT1 = factor(ACAT1, levels = c("0-6 Months", "6-12 Months", "+12 Months")))


ADAE <- ex_adae |> select(USUBJID, ARM, AEBODSYS, AEDECOD, ASTDY)

ADAE <- ADAE |>
  mutate(TRTEMFL = "Y") |>
  mutate(ACAT1 = case_when(
    ASTDY < 183 ~ "0-6 Months",
    ASTDY < 366 ~ "6-12 Months",
    TRUE ~ "+12 Months"
  )) |>
  mutate(ACAT1 = factor(ACAT1, levels = c("0-6 Months", "6-12 Months", "+12 Months")))

combodf <- tribble(
  ~valname, ~label, ~levelcombo, ~exargs,
  "Tot", "Total", c("0-6 Months", "6-12 Months", "+12 Months"), list(),
  "A_0-6 Months", "0-6 Months", c("0-6 Months", "6-12 Months", "+12 Months"), list(),
  "B_6-12 Months", "6-12 Months", c("6-12 Months", "+12 Months"), list(),
  "C_+12 Months", "+12 Months", c("+12 Months"), list()
)


lyt <- basic_table(show_colcounts = TRUE) |>
  split_cols_by("ARM") |>
  split_cols_by("ACAT1",
    split_fun = add_combo_levels(combosdf = combodf, trim = FALSE, keep_levels = combodf$valname)
  ) |>
  analyze("TRTEMFL",
    show_labels = "hidden",
    afun = a_freq_combos_j,
    extra_args = list(
      val = "Y",
      label = "Subjects with >= 1 AE",
      combosdf = combodf,
      filter_var = "ACAT1",
      do_not_filter = "Tot"
    )
  )


result <- build_table(lyt, df = ADAE, alt_counts_df = ADSL)

result
#>                                                     A: Drug X                                                            B: Placebo                                                         C: Combination                          
#>                              Total          0-6 Months      6-12 Months       +12 Months          Total          0-6 Months      6-12 Months       +12 Months          Total          0-6 Months      6-12 Months       +12 Months  
#>                             (N=134)          (N=134)          (N=123)          (N=117)           (N=134)          (N=134)          (N=125)          (N=113)           (N=132)          (N=132)          (N=124)          (N=111)    
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Subjects with >= 1 AE   122/134 (91.0%)   96/134 (71.6%)   75/123 (61.0%)   86/117 (73.5%)   123/134 (91.8%)   95/134 (70.9%)   85/125 (68.0%)   87/113 (77.0%)   120/132 (90.9%)   96/132 (72.7%)   77/124 (62.1%)   89/111 (80.2%)