TSFAE23C

Subjects With Related Treatment-emergent Adverse Events Leading to Discontinuation of Study Treatment by Preferred Term

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Code

# Program Name:              tsfae23c.R

# Prep environment

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

# Define script level parameters

tblid <- "TSFAE23c"
fileid <- tblid
popfl <- "SAFFL"
trtvar <- "TRT01A"
aerelvar <- "AEREL"
combined_colspan_trt <- TRUE
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map


if (combined_colspan_trt == TRUE) {
  # If no combined treatment column(s) needed for your study then this next
  # section of code can be removed
  # 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") %>%
  create_colspan_var(
    non_active_grp = c("Placebo"),
    non_active_grp_span_lbl = " ",
    active_grp_span_lbl = "Active Study Agent",
    colspan_var = "colspan_trt",
    trt_var = trtvar
  ) %>%
  select(
    STUDYID,
    USUBJID,
    !!rlang::sym(popfl),
    !!rlang::sym(trtvar),
    colspan_trt
  )

trt_map <- create_colspan_map(
  df = adsl,
  non_active_grp = c("Placebo"),
  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, "Placebo")

adae0 <- pharmaverseadamjnj::adae %>%
  filter(
    !!rlang::sym(popfl) == "Y" &
      !!rlang::sym(aerelvar) == "RELATED" &
      TRTEMFL == "Y" &
      AEACN == "DRUG WITHDRAWN"
  ) %>%
  left_join(
    subset(adsl, select = c("STUDYID", "USUBJID", "colspan_trt")),
    by = c("STUDYID", "USUBJID")
  ) %>%
  select(
    STUDYID,
    USUBJID,
    !!rlang::sym(trtvar),
    !!rlang::sym(popfl),
    !!rlang::sym(aerelvar),
    TRTEMFL,
    AEACN,
    AEDECOD,
    colspan_trt
  )

if (nrow(adae0) == 0) {
  adae <- adae0 %>%
    select(STUDYID, USUBJID, TRTEMFL, AEDECOD) %>%
    right_join(adsl, by = c("STUDYID", "USUBJID"))
} else {
  adae <- adae0
}

# 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 = trt_map))

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

lyt <- lyt %>%
  analyze(
    vars = "TRTEMFL",
    show_labels = "hidden",
    afun = a_freq_j,
    extra_args = list(
      label = "Subjects with >=1 related AE leading to discontinuation",
      .stats = c("count_unique_fraction"),
      val = "Y"
    ),
    section_div = c(" ")
  )

if (nrow(adae0) > 0) {
  lyt <- lyt %>%
    count_occurrences(
      vars = "AEDECOD",
      .stats = c("count_fraction_fixed_dp"),
      .indent_mods = c(count_fraction = -1L),
      .formats = c("count_fraction_fixed_dp" = jjcsformat_count_fraction),
      nested = FALSE
    ) %>%
    append_topleft("Preferred Term, n (%)")
} else {
  lyt <- lyt %>%
    append_topleft("Preferred Term, n (%)")
}

result <- build_table(lyt, df = adae, alt_counts_df = adsl)

# Post-processing

if (nrow(adae0) > 0) {
  # Post-Processing step to sort by descending count in the Combined
  # Xanomeline High Dose column
  result <- sort_at_path(
    tt = result,
    path = c("root", "AEDECOD"),
    scorefun = jj_complex_scorefun(
      spanningheadercolvar = "colspan_trt",
      colpath = NULL
    )
  )
}

if (nrow(adae0) == 0) {
  # Post-Processing step to remove 'Subjects with' row
  result <- safe_prune_table(
    result,
    prune_func = remove_rows(
      removerowtext = "Subjects with >=1 related AE leading to discontinuation"
    )
  )
}

# Retrieve 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)

TSFAE23c: Subjects With Related Treatment-emergent Adverse Events Leading to Discontinuation of Study Treatment by Preferred Term; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo
Preferred Term, n (%)	N=53	N=73	N=126	N=59
Subjects with ≥1 related AE leading to discontinuation	3 (5.7%)	5 (6.8%)	8 (6.3%)	7 (11.9%)

ASTHENIA	0	1 (1.4%)	1 (0.8%)	0
CHEST PAIN	1 (1.9%)	0	1 (0.8%)	0
CONTUSION	0	1 (1.4%)	1 (0.8%)	0
DIZZINESS	0	1 (1.4%)	1 (0.8%)	0
DYSPHONIA	0	1 (1.4%)	1 (0.8%)	0
HEADACHE	1 (1.9%)	0	1 (0.8%)	0
NASAL CONGESTION	1 (1.9%)	0	1 (0.8%)	0
PALPITATIONS	0	1 (1.4%)	1 (0.8%)	0
DISORIENTATION	0	0	0	1 (1.7%)
ELECTROCARDIOGRAM T WAVE AMPLITUDE DECREASED	0	0	0	1 (1.7%)
EYE PRURITUS	0	0	0	1 (1.7%)
HYPERHIDROSIS	0	0	0	1 (1.7%)
OEDEMA PERIPHERAL	0	0	0	1 (1.7%)
RASH	0	0	0	1 (1.7%)
UPPER RESPIRATORY TRACT INFECTION	0	0	0	1 (1.7%)

Note: An AE is assessed by the investigator as related. 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.

Download RTF file

--- title: TSFAE23C subtitle: Subjects With Related Treatment-emergent Adverse Events Leading to Discontinuation of Study Treatment by Preferred Term --- ------------------------------------------------------------------------ {{< include ../../_utils/envir_hook.qmd >}} ```{r setup, echo = FALSE, warning = FALSE, message = FALSE} options(docx.add_datetime = FALSE, tidytlg.add_datetime = FALSE) envsetup_config_name <- "default" # Path to the combined config file envsetup_file_path <- file.path("../..", "envsetup.yml") Sys.setenv(ENVSETUP_ENVIRON = '') library(envsetup) loaded_config <- config::get(config = envsetup_config_name, file = envsetup_file_path) envsetup::rprofile(loaded_config) dpscomp <- compound dpspdr <- paste(protocol,dbrelease,rpteff,sep="__") aptcomp <- compound aptpdr <- paste(protocol,dbrelease,rpteff,sep="__") ###### Study specific updates (formerly in envre) dpscomp <- "standards" dpspdr <- "jjcs__NULL__jjcs - core" apt <- FALSE library(junco) default_str_map <- rbind(default_str_map, c("&ctcae", "5.0")) ``` ## Output :::: panel-tabset ## {{< fa regular file-lines sm fw >}} Preview ```{r variant1, results='hide', warning = FALSE, message = FALSE} # Program Name: tsfae23c.R # Prep environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters tblid <- "TSFAE23c" fileid <- tblid popfl <- "SAFFL" trtvar <- "TRT01A" aerelvar <- "AEREL" combined_colspan_trt <- TRUE tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map if (combined_colspan_trt == TRUE) { # If no combined treatment column(s) needed for your study then this next # section of code can be removed # 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") %>% create_colspan_var( non_active_grp = c("Placebo"), non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) %>% select( STUDYID, USUBJID, !!rlang::sym(popfl), !!rlang::sym(trtvar), colspan_trt ) trt_map <- create_colspan_map( df = adsl, non_active_grp = c("Placebo"), 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, "Placebo") adae0 <- pharmaverseadamjnj::adae %>% filter( !!rlang::sym(popfl) == "Y" & !!rlang::sym(aerelvar) == "RELATED" & TRTEMFL == "Y" & AEACN == "DRUG WITHDRAWN" ) %>% left_join( subset(adsl, select = c("STUDYID", "USUBJID", "colspan_trt")), by = c("STUDYID", "USUBJID") ) %>% select( STUDYID, USUBJID, !!rlang::sym(trtvar), !!rlang::sym(popfl), !!rlang::sym(aerelvar), TRTEMFL, AEACN, AEDECOD, colspan_trt ) if (nrow(adae0) == 0) { adae <- adae0 %>% select(STUDYID, USUBJID, TRTEMFL, AEDECOD) %>% right_join(adsl, by = c("STUDYID", "USUBJID")) } else { adae <- adae0 } # 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 = trt_map)) if (combined_colspan_trt == TRUE) { lyt <- lyt %>% split_cols_by(trtvar, split_fun = mysplit) } else { lyt <- lyt %>% split_cols_by(trtvar) } lyt <- lyt %>% analyze( vars = "TRTEMFL", show_labels = "hidden", afun = a_freq_j, extra_args = list( label = "Subjects with >=1 related AE leading to discontinuation", .stats = c("count_unique_fraction"), val = "Y" ), section_div = c(" ") ) if (nrow(adae0) > 0) { lyt <- lyt %>% count_occurrences( vars = "AEDECOD", .stats = c("count_fraction_fixed_dp"), .indent_mods = c(count_fraction = -1L), .formats = c("count_fraction_fixed_dp" = jjcsformat_count_fraction), nested = FALSE ) %>% append_topleft("Preferred Term, n (%)") } else { lyt <- lyt %>% append_topleft("Preferred Term, n (%)") } result <- build_table(lyt, df = adae, alt_counts_df = adsl) # Post-processing if (nrow(adae0) > 0) { # Post-Processing step to sort by descending count in the Combined # Xanomeline High Dose column result <- sort_at_path( tt = result, path = c("root", "AEDECOD"), scorefun = jj_complex_scorefun( spanningheadercolvar = "colspan_trt", colpath = NULL ) ) } if (nrow(adae0) == 0) { # Post-Processing step to remove 'Subjects with' row result <- safe_prune_table( result, prune_func = remove_rows( removerowtext = "Subjects with >=1 related AE leading to discontinuation" ) ) } # Retrieve 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) ``` ```{r result1, echo=FALSE, message=FALSE, warning=FALSE, test = list(result_v1 = "result")} tt_to_flextable_j(result, tblid, string_map = string_map) ``` [Download RTF file](`r paste0(tolower(tblid), '.rtf')`) ::::