TSFAE06B

Subjects With Treatment-emergent Adverse Events With Frequency =5% in Drug-treated Group and =2% More Often in Drug-treated Group Than Placebo-treated Group by Preferred Term

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Code

# Program Name:              tsfae06b.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSFAE06b"
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, AEDECOD)

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(
    "AEDECOD",
    afun = a_freq_j,
    extra_args = append(extra_args_rr, NULL),
    indent_mod = 0L
  ) %>%
  append_topleft("Preferred Term, 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("AEDECOD"), scorefun = jj_complex_scorefun())

  ################################################################################
  # Prune table to only keep those that meet x% criteria from specified columns
  # and y% more often than placebo
  ################################################################################

  result <- safe_prune_table(
    result,
    prune_func = bspt_pruner(
      fraction = 0.05,
      diff_from_control = 0.02,
      control = ctrl_grp
    )
  )
}

## 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")

TSFAE06b: Subjects With Treatment-emergent Adverse Events With Frequency =[5]% in [Drug-treated] Group and =[2]% More Often in [Drug-treated] Group Than [Placebo-treated] Group by Preferred Term; 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
Preferred Term, n (%)	N=53	N=73	N=126	N=59
PRURITUS	20 (37.7%)	14 (19.2%)	34 (27.0%)	6 (10.2%)	27.6 (12.4, 42.7)	9.0 (-2.9, 20.9)
APPLICATION SITE PRURITUS	13 (24.5%)	18 (24.7%)	31 (24.6%)	4 (6.8%)	17.7 (4.5, 31.0)	17.9 (6.1, 29.7)
APPLICATION SITE ERYTHEMA	10 (18.9%)	11 (15.1%)	21 (16.7%)	2 (3.4%)	15.5 (4.0, 27.0)	11.7 (2.3, 21.1)
ERYTHEMA	12 (22.6%)	8 (11.0%)	20 (15.9%)	6 (10.2%)	12.5 (-1.2, 26.1)	0.8 (-9.7, 11.3)
DIZZINESS	8 (15.1%)	8 (11.0%)	16 (12.7%)	0	15.1 (5.5, 24.7)	11.0 (3.8, 18.1)
APPLICATION SITE IRRITATION	8 (15.1%)	7 (9.6%)	15 (11.9%)	2 (3.4%)	11.7 (1.0, 22.4)	6.2 (-2.0, 14.4)
RASH	5 (9.4%)	9 (12.3%)	14 (11.1%)	4 (6.8%)	2.7 (-7.5, 12.8)	5.5 (-4.4, 15.5)
HYPERHIDROSIS	8 (15.1%)	4 (5.5%)	12 (9.5%)	2 (3.4%)	11.7 (1.0, 22.4)	2.1 (-4.9, 9.1)
SINUS BRADYCARDIA	7 (13.2%)	4 (5.5%)	11 (8.7%)	0	13.2 (4.1, 22.3)	5.5 (0.3, 10.7)
FATIGUE	5 (9.4%)	5 (6.8%)	10 (7.9%)	0	9.4 (1.6, 17.3)	6.8 (1.1, 12.6)
APPLICATION SITE VESICLES	4 (7.5%)	4 (5.5%)	8 (6.3%)	0	7.5 (0.4, 14.7)	5.5 (0.3, 10.7)
HEADACHE	5 (9.4%)	3 (4.1%)	8 (6.3%)	2 (3.4%)	6.0 (-3.1, 15.2)	0.7 (-5.8, 7.2)
SKIN IRRITATION	4 (7.5%)	4 (5.5%)	8 (6.3%)	3 (5.1%)	2.5 (-6.6, 11.5)	0.4 (-7.3, 8.1)
COUGH	3 (5.7%)	4 (5.5%)	7 (5.6%)	1 (1.7%)	4.0 (-3.1, 11.0)	3.8 (-2.4, 10.0)
NAUSEA	5 (9.4%)	2 (2.7%)	7 (5.6%)	0	9.4 (1.6, 17.3)	2.7 (-1.0, 6.5)
VOMITING	5 (9.4%)	2 (2.7%)	7 (5.6%)	0	9.4 (1.6, 17.3)	2.7 (-1.0, 6.5)
NASOPHARYNGITIS	3 (5.7%)	3 (4.1%)	6 (4.8%)	2 (3.4%)	2.3 (-5.5, 10.0)	0.7 (-5.8, 7.2)
SALIVARY HYPERSECRETION	4 (7.5%)	0	4 (3.2%)	0	7.5 (0.4, 14.7)	0.0 (0.0, 0.0)
NASAL CONGESTION	3 (5.7%)	0	3 (2.4%)	2 (3.4%)	2.3 (-5.5, 10.0)	-3.4 (-8.0, 1.2)

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

Download RTF file

--- title: TSFAE06B subtitle: Subjects With Treatment-emergent Adverse Events With Frequency =5% in Drug-treated Group and =2% More Often in Drug-treated Group Than Placebo-treated Group by 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: tsfae06b.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSFAE06b" 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, AEDECOD) 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$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp)) } # join data together ae <- adae %>% right_join(., adsl, by = c("USUBJID")) colspan_trt_map <- create_colspan_map( adsl, non_active_grp = ctrl_grp, non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) # Define layout and build table: ref_path <- c("colspan_trt", " ", "TRT01A", "Placebo") extra_args_rr <- list( method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) lyt <- basic_table( top_level_section_div = " ", show_colcounts = TRUE, colcount_format = "N=xx" ) %>% split_cols_by( "colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map) ) if (combined_colspan_trt == TRUE) { lyt <- lyt %>% split_cols_by(trtvar, split_fun = mysplit) } else { lyt <- lyt %>% split_cols_by(trtvar) } if (risk_diff == TRUE) { lyt <- lyt %>% split_cols_by("rrisk_header", nested = FALSE) %>% split_cols_by( trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels("Placebo") ) } lyt <- lyt %>% analyze( "AEDECOD", afun = a_freq_j, extra_args = append(extra_args_rr, NULL), indent_mod = 0L ) %>% append_topleft("Preferred Term, n (%)") result <- build_table(lyt, ae, alt_counts_df = adsl) # Post-Processing step to sort by descending count on chosen active treatment columns. if (length(adae$TRTEMFL) != 0) { result <- sort_at_path(result, c("AEDECOD"), scorefun = jj_complex_scorefun()) ################################################################################ # Prune table to only keep those that meet x% criteria from specified columns # and y% more often than placebo ################################################################################ result <- safe_prune_table( result, prune_func = bspt_pruner( fraction = 0.05, diff_from_control = 0.02, control = ctrl_grp ) ) } ## 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')`) ::::