TSFAE10

Subjects With Treatment-emergent Related Adverse Events With Frequency =X% in Any Treatment Group by Preferred Term

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Output

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# Program Name:              tsfae10.R

# Prep Environment

library(envsetup)
library(tern)
library(dplyr)
library(rtables)
library(junco)

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSFAE10"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map


trtvar <- "TRT01A"
popfl <- "SAFFL"

aerelvar <- "AEREL"

combined_colspan_trt <- TRUE
risk_diff <- TRUE
rr_method <- "wald"
ctrl_grp <- "Placebo"

if (combined_colspan_trt == TRUE) {
  # Set up levels and label for the required combined columns
  add_combo <- add_combo_facet(
    "Combined",
    label = "Combined",
    levels = c("Xanomeline High Dose", "Xanomeline Low Dose")
  )

  # choose if any facets need to be removed - e.g remove the combined column for placebo
  rm_combo_from_placebo <- cond_rm_facets(
    facets = "Combined",
    ancestor_pos = NA,
    value = " ",
    split = "colspan_trt"
  )

  mysplit <- make_split_fun(post = list(add_combo, rm_combo_from_placebo))
}

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(!!rlang::sym(popfl) == "Y") %>%
  select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl))

# define criteria for related AE
adae <- pharmaverseadamjnj::adae %>%
  filter(TRTEMFL == "Y" & !!rlang::sym(aerelvar) == "RELATED") %>%
  select(USUBJID, TRTEMFL, AEDECOD)

adsl$colspan_trt <- factor(
  ifelse(adsl[[trtvar]] == "Placebo", " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)

if (risk_diff == TRUE) {
  adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
  adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))
}

# join data together
ae <- adae %>% right_join(., adsl, by = c("USUBJID"))


colspan_trt_map <- create_colspan_map(
  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
)

# Define layout and build table:

ref_path <- c("colspan_trt", " ", "TRT01A", "Placebo")
extra_args_rr <- list(
  method = rr_method,
  ref_path = ref_path,
  .stats = c("count_unique_fraction")
)


lyt <- basic_table(
  top_level_section_div = " ",
  show_colcounts = TRUE,
  colcount_format = "N=xx"
) %>%
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  )

if (combined_colspan_trt == TRUE) {
  lyt <- lyt %>%
    split_cols_by(trtvar, split_fun = mysplit)
} else {
  lyt <- lyt %>%
    split_cols_by(trtvar)
}

if (risk_diff == TRUE) {
  lyt <- lyt %>%
    split_cols_by("rrisk_header", nested = FALSE) %>%
    split_cols_by(
      trtvar,
      labels_var = "rrisk_label",
      split_fun = remove_split_levels("Placebo")
    )
}

lyt <- lyt %>%
  analyze(
    "AEDECOD",
    afun = a_freq_j,
    extra_args = append(extra_args_rr, NULL),
    show_labels = "hidden"
  ) %>%
  append_topleft("Preferred Term, n (%)")

result <- build_table(lyt, ae, alt_counts_df = adsl)

if (length(adae$TRTEMFL) != 0) {
  #########################################################################################
  # Post-Processing step to sort by descending count on chosen active treatment columns.
  # Default is the last treatment (inc. Combined if applicable) under the active treatment
  # spanning header (defaulted to colspan_trt variable). See function documentation for
  # jj_complex_scorefun should your require a different sorting behavior.
  #########################################################################################

  result <- sort_at_path(result, c("AEDECOD"), scorefun = jj_complex_scorefun())

  ################################################################################
  # Prune table to only keep those that meet x% criteria on any treatment column
  ################################################################################
  more_than_x_percent <- has_fraction_in_any_col(
    atleast = 0.05,
    col_names = c(
      "Active Study Agent.Xanomeline High Dose",
      "Active Study Agent.Xanomeline Low Dose",
      " .Placebo"
    )
  )

  result <- safe_prune_table(result, keep_rows(more_than_x_percent))
}

## Remove any rogue null rows
result <- result %>%
  safe_prune_table(prune_func = keep_rows(keep_non_null_rows))

## Remove the N=xx column headers for the risk difference columns
result <- remove_col_count(result)

# Add titles and footnotes:

result <- set_titles(result, tab_titles)

# Convert to tbl file and output table

tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, orientation = "landscape")

TSFAE10: Subjects With Treatment-emergent Related Adverse Events With Frequency =[X]% in [Any Treatment Group] by Preferred Term; Safety Analysis Set (Study jjcs - core)
	Active Study Agent				Risk Difference (%) (95% CI)
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
Preferred Term, n (%)	N=53	N=73	N=126	N=59
DIZZINESS	4 (7.5%)	2 (2.7%)	6 (4.8%)	0	7.5 (0.4, 14.7)	2.7 (-1.0, 6.5)
UPPER RESPIRATORY TRACT  INFECTION	0	0	0	3 (5.1%)	-5.1 (-10.7, 0.5)	-5.1 (-10.7, 0.5)
Note: Subjects are counted only once for any given event, regardless of the number of times they actually experienced the event.
Note: Adverse events are coded using MedDRA version 26.0.

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