TSIDEM02

Subjects by Region, Country/Territory, and Site

Output

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Code

# Program Name:              tsidem02

# Prep environment:

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

# Define script level parameters:

tblid <- "TSIDEM02"
fileid <- tblid
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

popfls <- c("SAFFL", "ITTFL", "FASFL")
popfl <- popfls[1]
trtvar <- "TRT01P"
ctrl_grp <- "Placebo"

# Initial Read in of adsl dataset

adsl <- pharmaverseadamjnj::adsl

# Further script level parameters, after having read in main data

demog_vars <- c("REGION1", "COUNTRY", "SITEID")
## make it named vars so that demog_vars[xx] with xx subset of vars still works
names(demog_vars) <- demog_vars
## retrieve labels
demog_labels <- formatters::var_labels(adsl)[demog_vars]

### vars that have _decode version : use these instead of the original version
vars_decode <- paste0(demog_vars, "_DECODE")

demog_displ_vars <- tibble(orig = demog_vars, displ = vars_decode) %>%
  mutate(displ_exist = displ %in% names(adsl)) %>%
  mutate(finalvar = ifelse(displ_exist, displ, orig)) %>%
  pull(finalvar)

# Process data:

# filter and restrict to population of interest
adsl <- adsl %>%
  filter(.data[[popfl]] == "Y") %>%
  select(
    USUBJID,
    starts_with("TRT01"),
    all_of(popfls),
    all_of(unique(c(demog_vars, demog_displ_vars)))
  )


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

# to ensure alphabetical ordering, as COUNTRY_DECODE is factor with order according COUNTRY, which is alphabetical on 3-letter code
adsl$REGION1 <- factor(
  as.character(adsl$REGION1),
  levels = sort(unique(as.character(adsl$REGION1)))
)
adsl$COUNTRY_DECODE <- factor(
  as.character(adsl$COUNTRY_DECODE),
  levels = sort(unique(as.character(adsl$COUNTRY_DECODE)))
)

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:

lyt <- basic_table(
  show_colcounts = TRUE,
  colcount_format = "N=xx"
) %>%
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  ) %>%
  split_cols_by(trtvar) %>%
  add_overall_col("Total") %>%
  split_rows_by(
    "REGION1",
    split_label = "Region",
    split_fun = trim_levels_in_group("COUNTRY_DECODE"),
    label_pos = "topleft",
    section_div = " "
  ) %>%
  summarize_row_groups("REGION1") %>%
  split_rows_by(
    "COUNTRY_DECODE",
    split_label = "Country/Territory",
    split_fun = trim_levels_in_group("SITEID"),
    label_pos = "topleft",
    section_div = " "
  ) %>%
  summarize_row_groups("COUNTRY_DECODE") %>%
  analyze_vars("SITEID", denom = "N_col", .stats = c("count_fraction")) %>%
  append_topleft("    Site, n (%)")

result <- build_table(lyt, adsl)

# 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)

TSIDEM02: Subjects by Region, Country/Territory, and Site; Full Analysis Set (Study jjcs - core)
Region	Active Study Agent
Country/Territory	Xanomeline High Dose	Xanomeline Low Dose	Placebo	Total
Site, n (%)	N=64	N=62	N=59	N=185
NA	64 (100.0%)	62 (100.0%)	59 (100.0%)	185 (100.0%)
United States of America	64 (100.0%)	62 (100.0%)	59 (100.0%)	185 (100.0%)
701	12 (18.8%)	12 (19.4%)	11 (18.6%)	35 (18.9%)
702	0	1 (1.6%)	0	1 (0.5%)
703	4 (6.2%)	5 (8.1%)	5 (8.5%)	14 (7.6%)
704	6 (9.4%)	7 (11.3%)	6 (10.2%)	19 (10.3%)
705	5 (7.8%)	3 (4.8%)	2 (3.4%)	10 (5.4%)
706	0	1 (1.6%)	0	1 (0.5%)
707	0	0	1 (1.7%)	1 (0.5%)
708	6 (9.4%)	6 (9.7%)	4 (6.8%)	16 (8.6%)
709	4 (6.2%)	5 (8.1%)	6 (10.2%)	15 (8.1%)
710	8 (12.5%)	7 (11.3%)	7 (11.9%)	22 (11.9%)
711	1 (1.6%)	1 (1.6%)	1 (1.7%)	3 (1.6%)
713	1 (1.6%)	2 (3.2%)	3 (5.1%)	6 (3.2%)
714	2 (3.1%)	2 (3.2%)	2 (3.4%)	6 (3.2%)
715	2 (3.1%)	2 (3.2%)	1 (1.7%)	5 (2.7%)
716	8 (12.5%)	4 (6.5%)	7 (11.9%)	19 (10.3%)
717	2 (3.1%)	1 (1.6%)	1 (1.7%)	4 (2.2%)
718	3 (4.7%)	3 (4.8%)	2 (3.4%)	8 (4.3%)

Download RTF file

--- title: TSIDEM02 subtitle: Subjects by Region, Country/Territory, and Site --- ------------------------------------------------------------------------ {{< 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: tsidem02 # Prep environment: library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: tblid <- "TSIDEM02" fileid <- tblid titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map popfls <- c("SAFFL", "ITTFL", "FASFL") popfl <- popfls[1] trtvar <- "TRT01P" ctrl_grp <- "Placebo" # Initial Read in of adsl dataset adsl <- pharmaverseadamjnj::adsl # Further script level parameters, after having read in main data demog_vars <- c("REGION1", "COUNTRY", "SITEID") ## make it named vars so that demog_vars[xx] with xx subset of vars still works names(demog_vars) <- demog_vars ## retrieve labels demog_labels <- formatters::var_labels(adsl)[demog_vars] ### vars that have _decode version : use these instead of the original version vars_decode <- paste0(demog_vars, "_DECODE") demog_displ_vars <- tibble(orig = demog_vars, displ = vars_decode) %>% mutate(displ_exist = displ %in% names(adsl)) %>% mutate(finalvar = ifelse(displ_exist, displ, orig)) %>% pull(finalvar) # Process data: # filter and restrict to population of interest adsl <- adsl %>% filter(.data[[popfl]] == "Y") %>% select( USUBJID, starts_with("TRT01"), all_of(popfls), all_of(unique(c(demog_vars, demog_displ_vars))) ) adsl$colspan_trt <- factor( ifelse(adsl[[trtvar]] == ctrl_grp, " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) # to ensure alphabetical ordering, as COUNTRY_DECODE is factor with order according COUNTRY, which is alphabetical on 3-letter code adsl$REGION1 <- factor( as.character(adsl$REGION1), levels = sort(unique(as.character(adsl$REGION1))) ) adsl$COUNTRY_DECODE <- factor( as.character(adsl$COUNTRY_DECODE), levels = sort(unique(as.character(adsl$COUNTRY_DECODE))) ) 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: lyt <- basic_table( show_colcounts = TRUE, colcount_format = "N=xx" ) %>% split_cols_by( "colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map) ) %>% split_cols_by(trtvar) %>% add_overall_col("Total") %>% split_rows_by( "REGION1", split_label = "Region", split_fun = trim_levels_in_group("COUNTRY_DECODE"), label_pos = "topleft", section_div = " " ) %>% summarize_row_groups("REGION1") %>% split_rows_by( "COUNTRY_DECODE", split_label = "Country/Territory", split_fun = trim_levels_in_group("SITEID"), label_pos = "topleft", section_div = " " ) %>% summarize_row_groups("COUNTRY_DECODE") %>% analyze_vars("SITEID", denom = "N_col", .stats = c("count_fraction")) %>% append_topleft(" Site, n (%)") result <- build_table(lyt, adsl) # 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) ``` ```{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')`) ::::