TSFAE09

Subjects With Treatment-emergent Adverse Events by System Organ Class

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Code

# Program Name:              tsfae09.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSFAE09"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

trtvar <- "TRT01A"
popfl <- "SAFFL"
combined_colspan_trt <- TRUE
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") %>%
  select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl))

adae <- pharmaverseadamjnj::adae %>%
  filter(TRTEMFL == "Y") %>%
  select(USUBJID, TRTEMFL, AEBODSYS)

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

if (risk_diff == TRUE) {
  adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
  adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))
}

# join data together
ae <- adae %>% right_join(., adsl, by = c("USUBJID"))

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:

ref_path <- c("colspan_trt", " ", "TRT01A", "Placebo")
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")
    )
}

lyt <- lyt %>%
  analyze(
    "AEBODSYS",
    afun = a_freq_j,
    extra_args = append(extra_args_rr, NULL),
    indent_mod = 0L
  ) %>%
  append_topleft("System Organ Class, n (%)")

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

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

if (length(adae$TRTEMFL) != 0) {
  result <- sort_at_path(
    result,
    c("AEBODSYS"),
    scorefun = jj_complex_scorefun()
  )
}

## Remove the N=xx column headers for the risk difference columns
result <- remove_col_count(result)

## Remove any rogue null rows
result <- result %>%
  safe_prune_table(prune_func = keep_rows(keep_non_null_rows))

# 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")

TSFAE09: Subjects With Treatment-emergent Adverse Events by System Organ Class; Safety Analysis Set (Study jjcs - core)
	Active Study Agent				Risk Difference (%) (95% CI)
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
System Organ Class, n (%)	N=53	N=73	N=126	N=59
General disorders and administration site conditions	26 (49.1%)	38 (52.1%)	64 (50.8%)	16 (27.1%)	21.9 (4.3, 39.5)	24.9 (8.8, 41.1)
Skin and subcutaneous tissue disorders	34 (64.2%)	28 (38.4%)	62 (49.2%)	16 (27.1%)	37.0 (19.8, 54.2)	11.2 (-4.7, 27.1)
Nervous system disorders	18 (34.0%)	18 (24.7%)	36 (28.6%)	5 (8.5%)	25.5 (10.9, 40.1)	16.2 (4.0, 28.4)
Gastrointestinal disorders	14 (26.4%)	10 (13.7%)	24 (19.0%)	7 (11.9%)	14.6 (0.1, 29.0)	1.8 (-9.6, 13.2)
Cardiac disorders	11 (20.8%)	11 (15.1%)	22 (17.5%)	7 (11.9%)	8.9 (-4.8, 22.6)	3.2 (-8.4, 14.8)
Infections and infestations	9 (17.0%)	6 (8.2%)	15 (11.9%)	14 (23.7%)	-6.7 (-21.6, 8.1)	-15.5 (-28.1, -3.0)
Psychiatric disorders	6 (11.3%)	8 (11.0%)	14 (11.1%)	7 (11.9%)	-0.5 (-12.4, 11.3)	-0.9 (-11.8, 10.0)
Respiratory, thoracic and mediastinal disorders	7 (13.2%)	6 (8.2%)	13 (10.3%)	5 (8.5%)	4.7 (-6.8, 16.3)	-0.3 (-9.8, 9.2)
Investigations	5 (9.4%)	7 (9.6%)	12 (9.5%)	7 (11.9%)	-2.4 (-13.8, 9.0)	-2.3 (-12.9, 8.4)
Musculoskeletal and connective tissue disorders	4 (7.5%)	5 (6.8%)	9 (7.1%)	2 (3.4%)	4.2 (-4.3, 12.6)	3.5 (-3.9, 10.9)
Injury, poisoning and procedural complications	3 (5.7%)	5 (6.8%)	8 (6.3%)	2 (3.4%)	2.3 (-5.5, 10.0)	3.5 (-3.9, 10.9)
Renal and urinary disorders	2 (3.8%)	3 (4.1%)	5 (4.0%)	2 (3.4%)	0.4 (-6.5, 7.3)	0.7 (-5.8, 7.2)
Vascular disorders	1 (1.9%)	3 (4.1%)	4 (3.2%)	3 (5.1%)	-3.2 (-9.9, 3.5)	-1.0 (-8.2, 6.2)
Ear and labyrinth disorders	1 (1.9%)	2 (2.7%)	3 (2.4%)	1 (1.7%)	0.2 (-4.7, 5.1)	1.0 (-3.9, 6.0)
Eye disorders	1 (1.9%)	2 (2.7%)	3 (2.4%)	2 (3.4%)	-1.5 (-7.4, 4.4)	-0.7 (-6.6, 5.3)
Reproductive system and breast disorders	3 (5.7%)	0	3 (2.4%)	3 (5.1%)	0.6 (-7.8, 8.9)	-5.1 (-10.7, 0.5)
Congenital, familial and genetic disorders	1 (1.9%)	1 (1.4%)	2 (1.6%)	0	1.9 (-1.8, 5.5)	1.4 (-1.3, 4.0)
Neoplasms benign, malignant and unspecified (incl cysts and polyps)	1 (1.9%)	1 (1.4%)	2 (1.6%)	0	1.9 (-1.8, 5.5)	1.4 (-1.3, 4.0)
Endocrine disorders	0	1 (1.4%)	1 (0.8%)	1 (1.7%)	-1.7 (-5.0, 1.6)	-0.3 (-4.6, 3.9)
Metabolism and nutrition disorders	1 (1.9%)	0	1 (0.8%)	2 (3.4%)	-1.5 (-7.4, 4.4)	-3.4 (-8.0, 1.2)
Surgical and medical procedures	1 (1.9%)	0	1 (0.8%)	2 (3.4%)	-1.5 (-7.4, 4.4)	-3.4 (-8.0, 1.2)
Hepatobiliary disorders	0	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Immune system disorders	0	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Social circumstances	0	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Note: Subjects are counted only once for any given SOC, regardless of the number of times they actually experienced an event within the SOC.
Note: Adverse events are coded using MedDRA version 26.0.

Download RTF file

--- title: TSFAE09 subtitle: Subjects With Treatment-emergent Adverse Events by System Organ Class --- ------------------------------------------------------------------------ {{< 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: tsfae09.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSFAE09" fileid <- tblid tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map trtvar <- "TRT01A" popfl <- "SAFFL" combined_colspan_trt <- TRUE 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") %>% select(STUDYID, USUBJID, all_of(trtvar), all_of(popfl)) adae <- pharmaverseadamjnj::adae %>% filter(TRTEMFL == "Y") %>% select(USUBJID, TRTEMFL, AEBODSYS) 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)" adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp)) } # join data together ae <- adae %>% right_join(., adsl, by = c("USUBJID")) colspan_trt_map <- create_colspan_map( adsl, non_active_grp = ctrl_grp, non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) # Define layout and build table: ref_path <- c("colspan_trt", " ", "TRT01A", "Placebo") extra_args_rr <- list( method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) lyt <- basic_table( top_level_section_div = " ", show_colcounts = TRUE, colcount_format = "N=xx" ) %>% split_cols_by( "colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map) ) if (combined_colspan_trt == TRUE) { lyt <- lyt %>% split_cols_by(trtvar, split_fun = mysplit) } else { lyt <- lyt %>% split_cols_by(trtvar) } if (risk_diff == TRUE) { lyt <- lyt %>% split_cols_by("rrisk_header", nested = FALSE) %>% split_cols_by( trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels("Placebo") ) } lyt <- lyt %>% analyze( "AEBODSYS", afun = a_freq_j, extra_args = append(extra_args_rr, NULL), indent_mod = 0L ) %>% append_topleft("System Organ Class, n (%)") result <- build_table(lyt, ae, alt_counts_df = adsl) # Post-Processing step to sort by descending count on chosen active treatment columns. if (length(adae$TRTEMFL) != 0) { result <- sort_at_path( result, c("AEBODSYS"), scorefun = jj_complex_scorefun() ) } ## Remove the N=xx column headers for the risk difference columns result <- remove_col_count(result) ## Remove any rogue null rows result <- result %>% safe_prune_table(prune_func = keep_rows(keep_non_null_rows)) # 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')`) ::::