TSIEX04

Number of Cycles Received

Output

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Code

# Program Name:              tsiex04.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSIEX04"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map


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

catlevels <- c("1 to <10", "10 to <20", ">=20")

combined_colspan_trt <- TRUE

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:

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

# If AVISIT is not present in ADEXSUM than create 'Overall' as the Visit, which is used
# for the filtering AVISIT records if it does exist
adexsum <- pharmaverseadamjnj::adexsum %>%
  mutate(VISIT = if (exists("AVISIT")) AVISIT else "Overall") %>%
  filter(PARAMCD == "NUMCYC" & !is.na(AVAL) & VISIT == "Overall") %>%
  select(USUBJID, PARAMCD, AVAL, AVALCAT1, starts_with("CRIT"))

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

# join data together
ex <- adexsum %>% inner_join(., adsl, by = c("USUBJID"))

# Keep only columns with some data in which will remove any unwanted CRITy variables
ex <- ex[, colSums(is.na(ex)) < nrow(ex)]

# Work out how many CRITy vars we have left
excritvars <- ex %>%
  select(num_range("CRIT", 1:99))

countcritvars <- length(names(excritvars))

# Drop unwanted levels for all CRITy variables you have remaining in ex and also for AVALCAT1
critlbls <- list()
for (i in 1:countcritvars) {
  variable_name <- paste0("CRIT", i)
  ex[[variable_name]] <- droplevels(ex[[variable_name]])
  critlbls[[i]] <- unique(as.character(ex[[variable_name]]))
}

ex$AVALCAT1 <- droplevels(ex$AVALCAT1)
ex$AVALCAT1 <- factor(ex$AVALCAT1, levels = catlevels)

colspan_trt_map <- create_colspan_map(
  adsl,
  non_active_grp = "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")

# Define layout and build table:

lyt <- rtables::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)
}

lyt <- lyt %>%
  analyze(
    "STUDYID",
    var_labels = "Total number of cycles received",
    afun = a_freq_j,
    extra_args = list(label = "N", .stats = "n_df"),
    indent_mod = 0L,
    show_labels = "visible"
  ) %>%
  analyze("AVAL", show_labels = "hidden", indent_mod = 2L, afun = function(x) {
    list(
      "Mean (SD)" = rcell(
        c(mean(x), sd(x)),
        format = jjcsformat_xx("xx.x (xx.xx)")
      ),
      "Median" = rcell(median(x), format = jjcsformat_xx("xx.x")),
      "Min, max" = rcell(c(min(x), max(x)), format = jjcsformat_xx("xx., xx."))
    )
  }) %>%
  analyze(
    "AVALCAT1",
    nested = FALSE,
    var_labels = "Total number of cycles received, n (%)",
    afun = a_freq_j,
    extra_args = list(denom = "n_df", .stats = c("count_unique_fraction")),
    indent_mod = 1L,
    show_labels = "visible"
  ) %>%
  analyze(
    "CRIT1FL",
    var_labels = "Total number of cycles received~[super a], n (%)",
    afun = a_freq_j,
    extra_args = list(
      val = "Y",
      label = critlbls[[1]],
      denom = "n_df",
      .stats = c("count_unique_fraction")
    ),
    indent_mod = 1L,
    show_labels = "visible",
    nested = FALSE
  )

# Add in analzye for all remaining CRIT variables contained in ex
for (i in 2:countcritvars) {
  lyt <- lyt %>%
    analyze(
      paste0("CRIT", i, "FL"),
      afun = a_freq_j,
      extra_args = list(
        val = "Y",
        label = critlbls[[i]],
        denom = "n_df",
        .stats = c("count_unique_fraction")
      ),
      indent_mod = 2L,
      show_labels = "hidden"
    )
}

lyt <- lyt %>%
  append_topleft("Parameter")

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

# 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 = "portrait")

TSIEX04: Number of Cycles Received; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo
Parameter	N=53	N=73	N=126	N=59
Total number of cycles received
N	53	73	126	59
Mean (SD)	12.1 (7.40)	12.9 (6.50)	12.5 (6.88)	12.0 (7.44)
Median	12.0	13.0	13.0	12.0
Min, max	0, 24	0, 24	0, 24	0, 24

Total number of cycles received, n (%)
1 to <10	19 (37.3%)	21 (29.6%)	40 (32.8%)	24 (42.1%)
10 to <20	21 (41.2%)	35 (49.3%)	56 (45.9%)	21 (36.8%)
≥20	11 (21.6%)	15 (21.1%)	26 (21.3%)	12 (21.1%)

Total number of cycles receiveda, n (%)
≥1	51 (96.2%)	71 (97.3%)	122 (96.8%)	57 (96.6%)
≥2	49 (92.5%)	69 (94.5%)	118 (93.7%)	56 (94.9%)
≥3	46 (86.8%)	69 (94.5%)	115 (91.3%)	54 (91.5%)
≥5	43 (81.1%)	66 (90.4%)	109 (86.5%)	48 (81.4%)
≥10	32 (60.4%)	50 (68.5%)	82 (65.1%)	33 (55.9%)

a Subjects are summarized in all applicable cycle categories.

Download RTF file

--- title: TSIEX04 subtitle: Number of Cycles Received --- ------------------------------------------------------------------------ {{< 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: tsiex04.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSIEX04" fileid <- tblid tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map trtvar <- "TRT01A" popfl <- "SAFFL" catlevels <- c("1 to <10", "10 to <20", ">=20") combined_colspan_trt <- TRUE 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: # Read in required data adsl <- pharmaverseadamjnj::adsl %>% filter(!!rlang::sym(popfl) == "Y") %>% select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl)) # If AVISIT is not present in ADEXSUM than create 'Overall' as the Visit, which is used # for the filtering AVISIT records if it does exist adexsum <- pharmaverseadamjnj::adexsum %>% mutate(VISIT = if (exists("AVISIT")) AVISIT else "Overall") %>% filter(PARAMCD == "NUMCYC" & !is.na(AVAL) & VISIT == "Overall") %>% select(USUBJID, PARAMCD, AVAL, AVALCAT1, starts_with("CRIT")) adsl$colspan_trt <- factor( ifelse(adsl[[trtvar]] == "Placebo", " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) # join data together ex <- adexsum %>% inner_join(., adsl, by = c("USUBJID")) # Keep only columns with some data in which will remove any unwanted CRITy variables ex <- ex[, colSums(is.na(ex)) < nrow(ex)] # Work out how many CRITy vars we have left excritvars <- ex %>% select(num_range("CRIT", 1:99)) countcritvars <- length(names(excritvars)) # Drop unwanted levels for all CRITy variables you have remaining in ex and also for AVALCAT1 critlbls <- list() for (i in 1:countcritvars) { variable_name <- paste0("CRIT", i) ex[[variable_name]] <- droplevels(ex[[variable_name]]) critlbls[[i]] <- unique(as.character(ex[[variable_name]])) } ex$AVALCAT1 <- droplevels(ex$AVALCAT1) ex$AVALCAT1 <- factor(ex$AVALCAT1, levels = catlevels) colspan_trt_map <- create_colspan_map( adsl, non_active_grp = "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") # Define layout and build table: lyt <- rtables::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) } lyt <- lyt %>% analyze( "STUDYID", var_labels = "Total number of cycles received", afun = a_freq_j, extra_args = list(label = "N", .stats = "n_df"), indent_mod = 0L, show_labels = "visible" ) %>% analyze("AVAL", show_labels = "hidden", indent_mod = 2L, afun = function(x) { list( "Mean (SD)" = rcell( c(mean(x), sd(x)), format = jjcsformat_xx("xx.x (xx.xx)") ), "Median" = rcell(median(x), format = jjcsformat_xx("xx.x")), "Min, max" = rcell(c(min(x), max(x)), format = jjcsformat_xx("xx., xx.")) ) }) %>% analyze( "AVALCAT1", nested = FALSE, var_labels = "Total number of cycles received, n (%)", afun = a_freq_j, extra_args = list(denom = "n_df", .stats = c("count_unique_fraction")), indent_mod = 1L, show_labels = "visible" ) %>% analyze( "CRIT1FL", var_labels = "Total number of cycles received~[super a], n (%)", afun = a_freq_j, extra_args = list( val = "Y", label = critlbls[[1]], denom = "n_df", .stats = c("count_unique_fraction") ), indent_mod = 1L, show_labels = "visible", nested = FALSE ) # Add in analzye for all remaining CRIT variables contained in ex for (i in 2:countcritvars) { lyt <- lyt %>% analyze( paste0("CRIT", i, "FL"), afun = a_freq_j, extra_args = list( val = "Y", label = critlbls[[i]], denom = "n_df", .stats = c("count_unique_fraction") ), indent_mod = 2L, show_labels = "hidden" ) } lyt <- lyt %>% append_topleft("Parameter") result <- build_table(lyt, ex, alt_counts_df = adsl) # 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 = "portrait") ``` ```{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')`) ::::