TSFAE19D

Subjects With Treatment-emergent Adverse Events by Female-specific OCMQ (Narrow) / (Broad) and Preferred Term

Output

Preview

Code

# Program Name:              tsfae19d.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSFAE19d"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

trtvar <- "TRT01A"
popfl <- "SAFFL"
sex <- "F"
ocmqclass <- "Broad"
ocmqflag <- "GENSPFFL"
ocmqnam_list <- c(
  "Abnormal Uterine Bleeding",
  "Amenorrhea",
  "Bacterial Vaginosis",
  "Decreased Menstrual Bleeding",
  "Excessive Menstrual Bleeding"
)
combined_colspan_trt <- FALSE
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" & SEX == sex) %>%
  select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl))

adae <- pharmaverseadamjnj::adaeocmq %>%
  filter(
    TRTEMFL == "Y" & OCMQCLSS == ocmqclass & (!!rlang::sym(ocmqflag) == "Y")
  ) %>%
  select(USUBJID, TRTEMFL, OCMQNAM, AEDECOD, !!rlang::sym(ocmqflag)) %>%
  mutate(
    OCMQNAM = factor(OCMQNAM, levels = union(levels(OCMQNAM), ocmqnam_list)),
  )

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

if (risk_diff == TRUE) {
  adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
  ### to avoid problems with level of trtvar not observed in main domain
  adsl$rrisk_label <- factor(
    paste(adsl[[trtvar]], paste("vs", ctrl_grp)),
    levels = paste(levels(adsl[[trtvar]]), paste("vs", ctrl_grp))
  )
}

# join data together
adae <- adae %>% inner_join(., adsl, by = intersect(names(adae), names(adsl)))

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:

# new approach to prevent label problems when treatment group not available in domain dataset
ctrl_grp2 <- paste(ctrl_grp, "vs", ctrl_grp)
ref_path <- c("colspan_trt", " ", "rrisk_label", ctrl_grp2)
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"))
    ### do not use labels_var, but rrisk_label as variable
    ### note updated level in remove_split_levels
    split_cols_by("rrisk_label", split_fun = remove_split_levels(ctrl_grp2))
}

lyt <- lyt %>%
  split_rows_by(
    "OCMQNAM",
    split_label = paste0("OCMQ (", ocmqclass, ")"),
    split_fun = keep_split_levels(ocmqnam_list),
    label_pos = "topleft",
    section_div = c(" "),
    child_labels = "hidden",
    nested = FALSE
  ) %>%
  summarize_row_groups(
    "OCMQNAM",
    cfun = a_freq_j,
    extra_args = extra_args_rr
  ) %>%
  analyze(
    "AEDECOD",
    afun = a_freq_j,
    extra_args = c(extra_args_rr, list(drop_levels = TRUE))
  ) %>%
  append_topleft("  Preferred Term, n (%)")

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

# If there is no data display "No data to display" text
if (nrow(adae) == 0) {
  result <- safe_prune_table(result)
}

# Post-Processing step to sort by descending count on chosen active treatment columns.

if (nrow(adae) != 0) {
  # result <- sort_at_path(result, c("OCMQNAM"), scorefun = jj_complex_scorefun())
  result <- sort_at_path(
    result,
    c("OCMQNAM", "*", "AEDECOD"),
    scorefun = jj_complex_scorefun()
  )
}

## note : perform this step after sorting, otherwise can result in errors (unable to find children AEDECOD)
## extra step : to remove lines with No data to report: note usage of trim_rows rather than prune_table
## this to ensure the content rows with empty levels are kept

prune_empty_level_tablerow <- function(tt) {
  if (is(tt, "ContentRow")) {
    return(FALSE)
  }
  if (is(tt, "TableRow")) {
    return(all_zero_or_na(tt))
  }
  kids <- tree_children(tt)
  length(kids) == 0
}

result <- result %>% trim_rows(prune_empty_level_tablerow)

## 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")

TSFAE19d: Subjects With Treatment-emergent Adverse Events by Female-specific FDA Medical Query (Broad) and Preferred Term; Female-specific Safety Analysis Set (Study jjcs - core)
	Active Study Agent			Risk Difference (%) (95% CI)
OCMQ (Broad)	Xanomeline High Dose	Xanomeline Low Dose	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
Preferred Term, n (%)	N=24	N=39	N=36
Abnormal Uterine Bleeding	0	1 (2.6%)	1 (2.8%)	-2.8 (-8.1, 2.6)	-0.2 (-7.5, 7.1)
BLEEDING ANOVULATORY	0	1 (2.6%)	1 (2.8%)	-2.8 (-8.1, 2.6)	-0.2 (-7.5, 7.1)

Amenorrhea	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Bacterial Vaginosis	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Decreased Menstrual Bleeding	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Excessive Menstrual Bleeding	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Key: FMQ=FDA Medical Query
Note: Adverse events are coded using MedDRA version 26.0.

Download RTF file

--- title: TSFAE19D subtitle: Subjects With Treatment-emergent Adverse Events by Female-specific OCMQ (Narrow) / (Broad) and 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: tsfae19d.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSFAE19d" fileid <- tblid tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map trtvar <- "TRT01A" popfl <- "SAFFL" sex <- "F" ocmqclass <- "Broad" ocmqflag <- "GENSPFFL" ocmqnam_list <- c( "Abnormal Uterine Bleeding", "Amenorrhea", "Bacterial Vaginosis", "Decreased Menstrual Bleeding", "Excessive Menstrual Bleeding" ) combined_colspan_trt <- FALSE 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" & SEX == sex) %>% select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl)) adae <- pharmaverseadamjnj::adaeocmq %>% filter( TRTEMFL == "Y" & OCMQCLSS == ocmqclass & (!!rlang::sym(ocmqflag) == "Y") ) %>% select(USUBJID, TRTEMFL, OCMQNAM, AEDECOD, !!rlang::sym(ocmqflag)) %>% mutate( OCMQNAM = factor(OCMQNAM, levels = union(levels(OCMQNAM), ocmqnam_list)), ) 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)" ### to avoid problems with level of trtvar not observed in main domain adsl$rrisk_label <- factor( paste(adsl[[trtvar]], paste("vs", ctrl_grp)), levels = paste(levels(adsl[[trtvar]]), paste("vs", ctrl_grp)) ) } # join data together adae <- adae %>% inner_join(., adsl, by = intersect(names(adae), names(adsl))) 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: # new approach to prevent label problems when treatment group not available in domain dataset ctrl_grp2 <- paste(ctrl_grp, "vs", ctrl_grp) ref_path <- c("colspan_trt", " ", "rrisk_label", ctrl_grp2) 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")) ### do not use labels_var, but rrisk_label as variable ### note updated level in remove_split_levels split_cols_by("rrisk_label", split_fun = remove_split_levels(ctrl_grp2)) } lyt <- lyt %>% split_rows_by( "OCMQNAM", split_label = paste0("OCMQ (", ocmqclass, ")"), split_fun = keep_split_levels(ocmqnam_list), label_pos = "topleft", section_div = c(" "), child_labels = "hidden", nested = FALSE ) %>% summarize_row_groups( "OCMQNAM", cfun = a_freq_j, extra_args = extra_args_rr ) %>% analyze( "AEDECOD", afun = a_freq_j, extra_args = c(extra_args_rr, list(drop_levels = TRUE)) ) %>% append_topleft(" Preferred Term, n (%)") result <- build_table(lyt, adae, alt_counts_df = adsl) # If there is no data display "No data to display" text if (nrow(adae) == 0) { result <- safe_prune_table(result) } # Post-Processing step to sort by descending count on chosen active treatment columns. if (nrow(adae) != 0) { # result <- sort_at_path(result, c("OCMQNAM"), scorefun = jj_complex_scorefun()) result <- sort_at_path( result, c("OCMQNAM", "*", "AEDECOD"), scorefun = jj_complex_scorefun() ) } ## note : perform this step after sorting, otherwise can result in errors (unable to find children AEDECOD) ## extra step : to remove lines with No data to report: note usage of trim_rows rather than prune_table ## this to ensure the content rows with empty levels are kept prune_empty_level_tablerow <- function(tt) { if (is(tt, "ContentRow")) { return(FALSE) } if (is(tt, "TableRow")) { return(all_zero_or_na(tt)) } kids <- tree_children(tt) length(kids) == 0 } result <- result %>% trim_rows(prune_empty_level_tablerow) ## 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") ``` ```{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')`) ::::