TSFAE20C

Demographic Characteristics for Subjects With Treatment-emergent Adverse Events - OCMQ of Interest / Preferred Term of Interest

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Code

# Program Name:              tsfae20c.R

# Prep Environment

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

# Define script level parameters:

tblid <- "TSFAE20c"
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map
fileid <- tblid
popfl <- "SAFFL"
trtvar <- "TRT01A"
subjFilterText <- "Abdominal Pain"
varname <- "OCMQNAM"
ctrl_grp <- "Placebo"

# Process Data

adsl <- pharmaverseadamjnj::adsl %>%
  filter(!!rlang::sym(popfl) == "Y") %>%
  create_colspan_var(
    non_active_grp = "Placebo",
    non_active_grp_span_lbl = " ",
    active_grp_span_lbl = "Active Study Agent",
    colspan_var = "colspan_trt",
    trt_var = trtvar
  ) %>%
  select(
    USUBJID,
    !!rlang::sym(popfl),
    !!rlang::sym(trtvar),
    SEX_DECODE,
    AGEGR1,
    RACE_DECODE,
    ETHNIC_DECODE,
    colspan_trt
  )

# Factor reformatting (e.g., Include missing in the "Unknown" category).
adsl$SEX_DECODE <- forcats::fct_na_value_to_level(
  adsl$SEX_DECODE,
  level = "Unknown"
)

adsl$AGEGR1_DECODE <- forcats::fct_na_value_to_level(
  factor(stringr::str_replace(as.character(adsl$AGEGR1), ">=", "\u2265")),
  level = "Unknown"
)

adsl$RACE_DECODE <- forcats::fct_collapse(
  forcats::fct_na_value_to_level(adsl$RACE_DECODE, level = "Unknown"),
  "Not reported or unknown" = c("Not reported", "Unknown")
)

adsl$ETHNIC_DECODE <- forcats::fct_collapse(
  forcats::fct_na_value_to_level(adsl$ETHNIC_DECODE, level = "Unknown"),
  "Not reported or unknown" = c("Not reported", "Unknown")
)

had_ae <- pharmaverseadamjnj::adaeocmq %>%
  filter(TRTEMFL == "Y" & CQ01NAM == "Seizure") %>%
  select(USUBJID, TRTEMFL) %>%
  distinct(USUBJID, .keep_all = TRUE)

adsl <- adsl %>%
  left_join(had_ae) %>%
  mutate(TRTEMFL = ifelse(is.na(TRTEMFL), "N", "Y"))

# Define layout and build table

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
)
ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp)

add_active_combo <- make_split_fun(
  post = list(
    add_combo_facet(
      name = "Combined",
      label = "Combined",
      levels = c("Xanomeline High Dose", "Xanomeline Low Dose")
    ),
    cond_rm_facets(
      facets = "Combined",
      ancestor_pos = NA,
      value = " ",
      split = "colspan_trt"
    )
  )
)

extra_args_rr <- list(
  riskdiff = FALSE
)

extra_args_rr2 <- append(
  extra_args_rr,
  list(resp_var = "TRTEMFL", drop_levels = TRUE)
)

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

lyt <- lyt %>%
  analyze(
    "TRTEMFL",
    afun = a_freq_j,
    extra_args = append(
      extra_args_rr,
      list(
        label = paste("Subjects with >= 1", subjFilterText),
        val = "Y",
        .stats = c("count_unique_fraction")
      )
    ),
    show_labels = "hidden"
  ) %>%
  analyze(
    vars = "SEX_DECODE",
    var_labels = "Sex, n/Ns (%)",
    show_labels = "visible",
    afun = a_freq_resp_var_j,
    extra_args = extra_args_rr2,
    nested = FALSE
  ) %>%
  analyze(
    vars = "AGEGR1_DECODE",
    var_labels = "Age group (years), n/Ns (%)",
    show_labels = "visible",
    afun = a_freq_resp_var_j,
    extra_args = extra_args_rr2,
    nested = FALSE
  ) %>%
  analyze(
    vars = "RACE_DECODE",
    var_labels = "Race, n/Ns (%)",
    show_labels = "visible",
    afun = a_freq_resp_var_j,
    extra_args = extra_args_rr2,
    nested = FALSE
  ) %>%
  analyze(
    vars = "ETHNIC_DECODE",
    var_labels = "Ethnicity, n/Ns (%)",
    show_labels = "visible",
    afun = a_freq_resp_var_j,
    extra_args = extra_args_rr2,
    nested = FALSE
  )

result <- build_table(lyt, adsl)

# Post-Processing:

result <- safe_prune_table(result, prune_func = count_pruner())

# Add titles and footnotes:

result <- set_titles(result, titles)

# Convert to tbl file and output table

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

TSFAE20c: Demographic Characteristics for Subjects With Treatment-emergent Adverse Events - [FDA Medical Query of Interest / Preferred Term of Interest]; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo
Characteristic	N=53	N=73	N=126	N=59
Subjects with ≥ 1 Abdominal Pain	46 (86.8%)	54 (74.0%)	100 (79.4%)	33 (55.9%)

Sex, n/Ns (%)
Male	28/29 (96.6%)	27/34 (79.4%)	55/63 (87.3%)	14/23 (60.9%)
Female	18/24 (75.0%)	27/39 (69.2%)	45/63 (71.4%)	19/36 (52.8%)

Age group (years), n/Ns (%)
≥18 to <65	7/8 (87.5%)	3/5 (60.0%)	10/13 (76.9%)	3/7 (42.9%)
≥65 to <75	12/16 (75.0%)	14/17 (82.4%)	26/33 (78.8%)	10/19 (52.6%)
≥75	27/29 (93.1%)	37/51 (72.5%)	64/80 (80.0%)	20/33 (60.6%)

Race, n/Ns (%)
American Indian or Alaska Native	5/5 (100.0%)	9/10 (90.0%)	14/15 (93.3%)	2/3 (66.7%)
Asian	9/10 (90.0%)	6/9 (66.7%)	15/19 (78.9%)	3/5 (60.0%)
Black or African American	3/3 (100.0%)	7/11 (63.6%)	10/14 (71.4%)	4/7 (57.1%)
Native Hawaiian or other Pacific Islander	6/7 (85.7%)	5/5 (100.0%)	11/12 (91.7%)	4/5 (80.0%)
White	7/8 (87.5%)	6/8 (75.0%)	13/16 (81.2%)	4/7 (57.1%)
Multiple	8/8 (100.0%)	7/10 (70.0%)	15/18 (83.3%)	1/5 (20.0%)
Not reported or unknown	7/11 (63.6%)	9/12 (75.0%)	16/23 (69.6%)	11/17 (64.7%)
Other	1/1 (100.0%)	5/8 (62.5%)	6/9 (66.7%)	4/10 (40.0%)

Ethnicity, n/Ns (%)
Hispanic or Latino	12/15 (80.0%)	12/15 (80.0%)	24/30 (80.0%)	6/12 (50.0%)
Not Hispanic or Latino	13/15 (86.7%)	12/18 (66.7%)	25/33 (75.8%)	10/16 (62.5%)
Not reported or unknown	21/23 (91.3%)	30/40 (75.0%)	51/63 (81.0%)	17/31 (54.8%)

Note: n=number of subjects with at least one event, Ns=total number of subjects for each specific subgroup

Download RTF file

--- title: TSFAE20C subtitle: Demographic Characteristics for Subjects With Treatment-emergent Adverse Events - OCMQ of Interest / Preferred Term of Interest --- ------------------------------------------------------------------------ {{< 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: tsfae20c.R # Prep Environment library(envsetup) library(tern) library(forcats) library(dplyr) library(rtables) library(junco) # Define script level parameters: tblid <- "TSFAE20c" titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map fileid <- tblid popfl <- "SAFFL" trtvar <- "TRT01A" subjFilterText <- "Abdominal Pain" varname <- "OCMQNAM" ctrl_grp <- "Placebo" # Process Data adsl <- pharmaverseadamjnj::adsl %>% filter(!!rlang::sym(popfl) == "Y") %>% create_colspan_var( non_active_grp = "Placebo", non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) %>% select( USUBJID, !!rlang::sym(popfl), !!rlang::sym(trtvar), SEX_DECODE, AGEGR1, RACE_DECODE, ETHNIC_DECODE, colspan_trt ) # Factor reformatting (e.g., Include missing in the "Unknown" category). adsl$SEX_DECODE <- forcats::fct_na_value_to_level( adsl$SEX_DECODE, level = "Unknown" ) adsl$AGEGR1_DECODE <- forcats::fct_na_value_to_level( factor(stringr::str_replace(as.character(adsl$AGEGR1), ">=", "\u2265")), level = "Unknown" ) adsl$RACE_DECODE <- forcats::fct_collapse( forcats::fct_na_value_to_level(adsl$RACE_DECODE, level = "Unknown"), "Not reported or unknown" = c("Not reported", "Unknown") ) adsl$ETHNIC_DECODE <- forcats::fct_collapse( forcats::fct_na_value_to_level(adsl$ETHNIC_DECODE, level = "Unknown"), "Not reported or unknown" = c("Not reported", "Unknown") ) had_ae <- pharmaverseadamjnj::adaeocmq %>% filter(TRTEMFL == "Y" & CQ01NAM == "Seizure") %>% select(USUBJID, TRTEMFL) %>% distinct(USUBJID, .keep_all = TRUE) adsl <- adsl %>% left_join(had_ae) %>% mutate(TRTEMFL = ifelse(is.na(TRTEMFL), "N", "Y")) # Define layout and build table 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 ) ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp) add_active_combo <- make_split_fun( post = list( add_combo_facet( name = "Combined", label = "Combined", levels = c("Xanomeline High Dose", "Xanomeline Low Dose") ), cond_rm_facets( facets = "Combined", ancestor_pos = NA, value = " ", split = "colspan_trt" ) ) ) extra_args_rr <- list( riskdiff = FALSE ) extra_args_rr2 <- append( extra_args_rr, list(resp_var = "TRTEMFL", drop_levels = TRUE) ) lyt <- basic_table( show_colcounts = TRUE, colcount_format = "N=xx", top_level_section_div = " " ) %>% append_topleft("Characteristic") %>% split_cols_by( "colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map) ) %>% split_cols_by(trtvar, split_fun = add_active_combo) lyt <- lyt %>% analyze( "TRTEMFL", afun = a_freq_j, extra_args = append( extra_args_rr, list( label = paste("Subjects with >= 1", subjFilterText), val = "Y", .stats = c("count_unique_fraction") ) ), show_labels = "hidden" ) %>% analyze( vars = "SEX_DECODE", var_labels = "Sex, n/Ns (%)", show_labels = "visible", afun = a_freq_resp_var_j, extra_args = extra_args_rr2, nested = FALSE ) %>% analyze( vars = "AGEGR1_DECODE", var_labels = "Age group (years), n/Ns (%)", show_labels = "visible", afun = a_freq_resp_var_j, extra_args = extra_args_rr2, nested = FALSE ) %>% analyze( vars = "RACE_DECODE", var_labels = "Race, n/Ns (%)", show_labels = "visible", afun = a_freq_resp_var_j, extra_args = extra_args_rr2, nested = FALSE ) %>% analyze( vars = "ETHNIC_DECODE", var_labels = "Ethnicity, n/Ns (%)", show_labels = "visible", afun = a_freq_resp_var_j, extra_args = extra_args_rr2, nested = FALSE ) result <- build_table(lyt, adsl) # Post-Processing: result <- safe_prune_table(result, prune_func = count_pruner()) # Add titles and footnotes: result <- set_titles(result, titles) # Convert to tbl file and output table tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, orientation = "landscape") ``` ```{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')`) ::::