junco (version 0.1.2)

a_patyrs_j: Patient years exposure

Description

Statistical/Analysis Function for presenting Patient years exposure summary data

Usage

s_patyrs_j(
  df,
  .var,
  id = "USUBJID",
  .alt_df_full,
  source = c("alt_df", "df"),
  inriskdiffcol = FALSE
)
a_patyrs_j(
  df,
  .var,
  .df_row,
  id = "USUBJID",
  .alt_df_full = NULL,
  .formats = NULL,
  .labels = NULL,
  source = c("alt_df", "df"),
  .spl_context,
  .stats = "patyrs"
)

Value

• s_patyrs_j() returns a list containing the patient years statistics. The list of available statistics for can be viewed by running junco_get_stats("a_patyrs_j"). Currently, this is just a single statistic patyrs, patient years of exposure.

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

Arguments

df

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

.var

(string)
variable name containing the patient years data.

id

(string)
subject variable name.

.alt_df_full

(dataframe)
alternative dataset for calculations.

source

(string)
source of data, either "alt_df" or "df".

inriskdiffcol

(logical)
flag indicating if the function is called within a risk difference column.

.df_row

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

.formats

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

.labels

(named 'character')
labels for the statistics.

.spl_context

(data.frame)
gives information about ancestor split states.

.stats

(character)
statistics to select for the table.

Functions

• s_patyrs_j(): Statistical Function for Patient years exposure summary data

• a_patyrs_j(): Formatted analysis function for patient years summary which is used as afun in analyze or cfun in summarize_row_groups.

Examples

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


adexsum <- ex_adsl |>
  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(
    rrisk_header = "Risk Difference (95% CI)",
    rrisk_label = paste(!!rlang::sym(trtvar), "vs", ctrl_grp),
    TRTDURY = case_when(
      !is.na(EOSDY) ~ EOSDY,
      TRUE ~ as.integer(cutoffd - as.Date(TRTSDTM) + 1)
    )
  ) |>
  select(USUBJID, !!rlang::sym(trtvar), colspan_trt, rrisk_header, rrisk_label, TRTDURY)

adae <- ex_adae |>
  group_by(USUBJID, AEDECOD) |>
  select(USUBJID, AEDECOD, ASTDY) |>
  mutate(rwnum = row_number()) |>
  mutate(AOCCPFL = case_when(
    rwnum == 1 ~ "Y",
    TRUE ~ NA
  )) |>
  filter(AOCCPFL == "Y")

aefup <- left_join(adae, adexsum, by = "USUBJID")

colspan_trt_map <- create_colspan_map(adexsum,
  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)

################################################################################
# Define layout and build table:
################################################################################

lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx", top_level_section_div = " ") |>
  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)) |>
  analyze("TRTDURY",
    nested = FALSE,
    show_labels = "hidden",
    afun = a_patyrs_j
  )
result <- build_table(lyt, aefup, alt_counts_df = adexsum)
result