TSIEX08

Incidence and Reason for Dose Modification

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Code

# Program Name:              tsiex08.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSIEX08"
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

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))

adex1 <- pharmaverseadamjnj::adex %>%
  filter(!grepl("UNSCHEDULED", AVISIT, ignore.case = TRUE))

# Convert AVISIT to sentence case and apply levels to maintain ordering
avisit_levs <- stringr::str_to_sentence(levels(adex1$AVISIT))
adex1$AVISIT <- stringr::str_to_sentence(adex1$AVISIT)
adex1$AVISIT <- factor(adex1$AVISIT, levels = avisit_levs)

adex2 <- pharmaverseadamjnj::adex %>%
  mutate(AVISIT = "Overall")

adex_ <- bind_rows(adex1, adex2)

adex1$AVISIT <- droplevels(adex1$AVISIT)
adex_$AVISIT <- factor(
  adex_$AVISIT,
  levels = c("Overall", levels(adex1$AVISIT))
)

adex <- adex_ %>%
  select(USUBJID, ACAT1, ACAT2, AREASOC, AADJ, AVISIT, AVISITN)

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

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

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
)

# Define layout and build table:

# Note :
# variables AREASOC and AADJ contain missing values, these are removed inside a_freq_j when using denom = "n_df"
# either use n_parentdf and denom_by = AVISIT to get proper denominator
# alternative would be to use ex <- df_explicit_na(ex, omit_columns = "colspan_trt", na_level = "<Missing>") and
# and have denom = "n_df" in extra_args1 and
# add excl_levels = "<Missing>" to the extra_args in the calls to these 2 variables
# excl_levels cannot be used in the overall extra_args1 as val and excl_levels cannot be used together

extra_args1 <- list(
  .stats = "count_unique_fraction",
  denom = "n_parentdf",
  denom_by = "AVISIT"
)

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 %>%
  split_rows_by(
    "AVISIT",
    split_label = "Time Point",
    split_fun = trim_levels_in_group("STUDYID"),
    label_pos = "topleft",
    indent_mod = 0L,
    section_div = c(" ")
  ) %>%
  summarize_row_groups(
    "AVISIT",
    cfun = a_freq_j,
    extra_args = list(.stats = "n_df"),
    indent_mod = 0L
  ) %>%
  analyze(
    "ACAT2",
    afun = a_freq_j,
    extra_args = append(
      extra_args1,
      list(label = "Dose not administered", val = "Dose not administered")
    ),
    indent_mod = 0L,
    show_labels = "hidden"
  ) %>%
  analyze(
    "AREASOC",
    afun = a_freq_j,
    extra_args = extra_args1,
    show_labels = "hidden",
    indent_mod = 1L
  ) %>%
  analyze(
    "ACAT1",
    afun = a_freq_j,
    extra_args = append(
      extra_args1,
      list(label = "Dose adjusted", val = "Dose adjusted")
    ),
    indent_mod = 0L,
    show_labels = "hidden"
  ) %>%
  analyze(
    "AADJ",
    afun = a_freq_j,
    extra_args = extra_args1,
    show_labels = "hidden",
    indent_mod = 1L
  ) %>%
  append_topleft("  Dose Modification, n (%)")

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

# Prune table to only keep those that have >0 counts in any treatment column specified

more_than_zero <- has_count_in_any_col(
  atleast = 1,
  col_names = c(
    "Active Study Agent.Xanomeline High Dose",
    "Active Study Agent.Xanomeline Low Dose",
    "Active Study Agent.Combined",
    " .Placebo"
  )
)

result <- safe_prune_table(result, keep_rows(more_than_zero))

# 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")

TSIEX08: Incidence and Reason for Dose Modification; Safety Analysis Set (Study jjcs - core)
	Active Study Agent
Time Point	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo
Dose Modification, n (%)	N=53	N=73	N=126	N=59
Overall	53	73	126	59
Dose not administered	47 (88.7%)	52 (71.2%)	99 (78.6%)	49 (83.1%)
Adverse Event	1 (1.9%)	0	1 (0.8%)	0
Other	1 (1.9%)	0	1 (0.8%)	0
Dose adjusted	1 (1.9%)	0	1 (0.8%)	0
Adverse Event	1 (1.9%)	0	1 (0.8%)	0
Other	1 (1.9%)	0	1 (0.8%)	0

Baseline	53	73	126	59
Dose not administered	28 (52.8%)	36 (49.3%)	64 (50.8%)	24 (40.7%)

Week 2	53	52	105	56
Dose not administered	28 (52.8%)	28 (53.8%)	56 (53.3%)	30 (53.6%)
Adverse Event	1 (1.9%)	0	1 (1.0%)	0
Dose adjusted	1 (1.9%)	0	1 (1.0%)	0
Adverse Event	1 (1.9%)	0	1 (1.0%)	0

Week 24	20	15	35	40
Dose not administered	9 (45.0%)	8 (53.3%)	17 (48.6%)	25 (62.5%)
Adverse Event	1 (5.0%)	0	1 (2.9%)	0
Other	1 (5.0%)	0	1 (2.9%)	0
Dose adjusted	1 (5.0%)	0	1 (2.9%)	0
Adverse Event	1 (5.0%)	0	1 (2.9%)	0
Other	1 (5.0%)	0	1 (2.9%)	0

Note: Percentages are based on the number of subjects who had a visit at the specified time point.
Note: Subjects may be counted in more than one category.

Download RTF file

--- title: TSIEX08 subtitle: Incidence and Reason for Dose Modification --- ------------------------------------------------------------------------ {{< 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: tsiex08.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSIEX08" 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 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)) adex1 <- pharmaverseadamjnj::adex %>% filter(!grepl("UNSCHEDULED", AVISIT, ignore.case = TRUE)) # Convert AVISIT to sentence case and apply levels to maintain ordering avisit_levs <- stringr::str_to_sentence(levels(adex1$AVISIT)) adex1$AVISIT <- stringr::str_to_sentence(adex1$AVISIT) adex1$AVISIT <- factor(adex1$AVISIT, levels = avisit_levs) adex2 <- pharmaverseadamjnj::adex %>% mutate(AVISIT = "Overall") adex_ <- bind_rows(adex1, adex2) adex1$AVISIT <- droplevels(adex1$AVISIT) adex_$AVISIT <- factor( adex_$AVISIT, levels = c("Overall", levels(adex1$AVISIT)) ) adex <- adex_ %>% select(USUBJID, ACAT1, ACAT2, AREASOC, AADJ, AVISIT, AVISITN) adsl$colspan_trt <- factor( ifelse(adsl[[trtvar]] == "Placebo", " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) # join data together ex <- adex %>% inner_join(., adsl, by = c("USUBJID")) 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 ) # Define layout and build table: # Note : # variables AREASOC and AADJ contain missing values, these are removed inside a_freq_j when using denom = "n_df" # either use n_parentdf and denom_by = AVISIT to get proper denominator # alternative would be to use ex <- df_explicit_na(ex, omit_columns = "colspan_trt", na_level = "<Missing>") and # and have denom = "n_df" in extra_args1 and # add excl_levels = "<Missing>" to the extra_args in the calls to these 2 variables # excl_levels cannot be used in the overall extra_args1 as val and excl_levels cannot be used together extra_args1 <- list( .stats = "count_unique_fraction", denom = "n_parentdf", denom_by = "AVISIT" ) 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 %>% split_rows_by( "AVISIT", split_label = "Time Point", split_fun = trim_levels_in_group("STUDYID"), label_pos = "topleft", indent_mod = 0L, section_div = c(" ") ) %>% summarize_row_groups( "AVISIT", cfun = a_freq_j, extra_args = list(.stats = "n_df"), indent_mod = 0L ) %>% analyze( "ACAT2", afun = a_freq_j, extra_args = append( extra_args1, list(label = "Dose not administered", val = "Dose not administered") ), indent_mod = 0L, show_labels = "hidden" ) %>% analyze( "AREASOC", afun = a_freq_j, extra_args = extra_args1, show_labels = "hidden", indent_mod = 1L ) %>% analyze( "ACAT1", afun = a_freq_j, extra_args = append( extra_args1, list(label = "Dose adjusted", val = "Dose adjusted") ), indent_mod = 0L, show_labels = "hidden" ) %>% analyze( "AADJ", afun = a_freq_j, extra_args = extra_args1, show_labels = "hidden", indent_mod = 1L ) %>% append_topleft(" Dose Modification, n (%)") result <- build_table(lyt, ex, alt_counts_df = adsl) # Prune table to only keep those that have >0 counts in any treatment column specified more_than_zero <- has_count_in_any_col( atleast = 1, col_names = c( "Active Study Agent.Xanomeline High Dose", "Active Study Agent.Xanomeline Low Dose", "Active Study Agent.Combined", " .Placebo" ) ) result <- safe_prune_table(result, keep_rows(more_than_zero)) # 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')`) ::::