TSIEX01

Duration of Treatment

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# Program Name:              tsiex01.R

# Prep Environment

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

# Define script level parameters:

# - Define output ID and file location

tblid <- "TSIEX01"
fileid <- tblid
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map


trtvar <- "TRT01A"
popfl <- "SAFFL"
ctrl_grp <- "Placebo"

comp_btw_group <- TRUE
ancova <- TRUE

trtdur <- "TRTDURM"
daysconv <- 30.4375
catlevels <- c(
  "0 to <3 months",
  "3 to <6 months",
  "6 to <9 months",
  "9 to <12 months",
  "12 to <15 months",
  "15 to <18 months",
  "18 to <21 months",
  "21 to <24 months",
  "24 to <27 months",
  "27 to <30 months",
  "30 to <33 months",
  "33 to <36 months",
  "36 to <39 months"
)

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 == trtdur & !is.na(AVAL) & VISIT == "Overall") %>%
  mutate(
    CRIT0FL = "Y",
    CRIT0 = as.factor("Any duration (at least 1 dose)")
  ) %>%
  select(USUBJID, PARAMCD, AVAL, AVALCAT1, starts_with("CRIT"))

adexsum$CRIT0FL <- factor(adexsum$CRIT0FL, levels = c("Y", "N"))

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

adsl$diff_header <- "Difference in Means (95% CI)"
adsl$diff_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))

# 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 (ignoring CRIT0 we created) 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

for (i in 1:countcritvars) {
  variable_name <- paste0("CRIT", i)
  ex[[variable_name]] <- droplevels(ex[[variable_name]])
}

# drop unwanted levels from AVALCAT1 and assign levels from specified section at the top of script
ex$AVALCAT1 <- droplevels(ex$AVALCAT1)
ex$AVALCAT1 <- factor(ex$AVALCAT1, levels = catlevels)

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
)
ref_path <- c("colspan_trt", "", trtvar, ctrl_grp)

# Create label_map to be utilized in the Criterion variables (CRIT)

ex_lblmap <- ex %>%
  select(-CRIT0, -CRIT0FL) %>%
  tidyr::pivot_longer(
    cols = starts_with("CRIT") & ends_with("FL"),
    names_to = "value",
    values_to = "CF_value"
  ) %>%
  # Create the label by selecting the corresponding values from the CRIT columns
  mutate(
    label = case_when(
      value == "CRIT1FL" ~ CRIT1,
      value == "CRIT2FL" ~ CRIT2,
      value == "CRIT3FL" ~ CRIT3,
      value == "CRIT4FL" ~ CRIT4,
      value == "CRIT5FL" ~ CRIT5,
      value == "CRIT6FL" ~ CRIT6,
      value == "CRIT7FL" ~ CRIT7
    )
  ) %>%
  select(value, label) %>%
  distinct()

# Define layout and build table:

extra_args_rr <- list(.stats = c("count_unique_fraction"), riskdiff = FALSE)

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_cols_by("diff_header", nested = FALSE) %>%
  split_cols_by(
    trtvar,
    split_fun = remove_split_levels(ctrl_grp),
    labels_var = "diff_label"
  ) %>%
  analyze(
    "AVAL",
    afun = a_summarize_ex_j,
    var_labels = "Duration~[super a], ([unit])", #TODO FIX THIS
    show_labels = "visible",
    indent_mod = 0L,
    extra_args = list(
      daysconv = daysconv,
      ref_path = ref_path,
      ancova = ancova,
      comp_btw_group = comp_btw_group,
      variables = list(arm = trtvar, covariates = NULL),
      .formats = c(
        range = jjcsformat_xx("xx.x, xx.x"),
        "diff_mean_est_ci" = jjcsformat_xx("xx.xx (xx.xx, xx.xx)")
      )
    )
  ) %>%
  analyze(
    "AVALCAT1",
    var_labels = "Duration of treatment, n (%)",
    afun = a_freq_j,
    extra_args = extra_args_rr,
    indent_mod = 1L,
    show_labels = "visible",
    nested = FALSE
  ) %>%
  analyze(
    paste0("CRIT", 1, "FL"),
    var_labels = "Duration~[super b], n (%)", #TODO FIX THIS
    afun = a_freq_j,
    extra_args = append(
      extra_args_rr,
      list(label = as.character(ex_lblmap$label[1]), val = "Y")
    ),
    indent_mod = 1L,
    show_labels = "visible",
    nested = FALSE
  )

# Add in analyze for all others CRIT variables contained in ex
for (i in 2:countcritvars) {
  lyt <- lyt %>%
    analyze(
      paste0("CRIT", i, "FL"),
      afun = a_freq_j,
      extra_args = append(
        extra_args_rr,
        list(label = as.character(ex_lblmap$label[i]), val = "Y")
      ),
      indent_mod = 2L,
      show_labels = "hidden"
    )
}

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

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

## Remove the N=xx column headers for the Difference (95% CI) columns
result <- remove_col_count(result, span_label_var = "diff_header")

# 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")

TSIEX01: Duration of Treatment; Safety Analysis Set (Study jjcs - core)
	Active Study Agent				Difference in Means (95% CI)
	Xanomeline High Dose	Xanomeline Low Dose	Combined	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
Parameter	N=53	N=73	N=126	N=59
Durationa, ([unit])
Mean (SD)	17.41 (11.408)	20.46 (10.198)	19.18 (10.786)	18.64 (10.656)	-1.23 (-5.23, 2.77)	1.82 (-1.88, 5.52)
Median	16.10	21.20	18.90	18.60
Min, max	0.2, 35.4	0.1, 37.9	0.1, 37.9	0.9, 37.2
Interquartile range	8.30, 27.80	14.70, 28.10	11.30, 28.00	9.30, 26.30
Total treatment (subject years)	922.9 (76.9)	1493.8 (124.5)	2416.7 (201.4)	1099.9 (91.7)

Duration of treatment, n (%)
0 to <3 months	7 (13.2%)	4 (5.5%)	11 (8.7%)	2 (3.4%)
3 to <6 months	5 (9.4%)	5 (6.8%)	10 (7.9%)	5 (8.5%)
6 to <9 months	3 (5.7%)	3 (4.1%)	6 (4.8%)	7 (11.9%)
9 to <12 months	4 (7.5%)	2 (2.7%)	6 (4.8%)	7 (11.9%)
12 to <15 months	6 (11.3%)	8 (11.0%)	14 (11.1%)	5 (8.5%)
15 to <18 months	3 (5.7%)	10 (13.7%)	13 (10.3%)	3 (5.1%)
18 to <21 months	5 (9.4%)	4 (5.5%)	9 (7.1%)	5 (8.5%)
21 to <24 months	1 (1.9%)	7 (9.6%)	8 (6.3%)	5 (8.5%)
24 to <27 months	3 (5.7%)	6 (8.2%)	9 (7.1%)	6 (10.2%)
27 to <30 months	6 (11.3%)	9 (12.3%)	15 (11.9%)	2 (3.4%)
30 to <33 months	5 (9.4%)	4 (5.5%)	9 (7.1%)	3 (5.1%)
33 to <36 months	5 (9.4%)	9 (12.3%)	14 (11.1%)	5 (8.5%)
36 to <39 months	0	2 (2.7%)	2 (1.6%)	4 (6.8%)

Durationb, n (%)
<1 month	4 (7.5%)	1 (1.4%)	5 (4.0%)	1 (1.7%)
≥1 month	49 (92.5%)	72 (98.6%)	121 (96.0%)	58 (98.3%)
≥2 months	46 (86.8%)	71 (97.3%)	117 (92.9%)	58 (98.3%)
≥3 months	46 (86.8%)	69 (94.5%)	115 (91.3%)	57 (96.6%)
≥6 months	41 (77.4%)	64 (87.7%)	105 (83.3%)	52 (88.1%)
≥12 months	34 (64.2%)	59 (80.8%)	93 (73.8%)	38 (64.4%)
≥24 months	19 (35.8%)	30 (41.1%)	49 (38.9%)	20 (33.9%)

a Duration of treatment is defined as [(date of last dose of study treatment – date of first dose of study treatment) +1].
b Subjects are summarized in all applicable duration categories.

Download RTF file

--- title: TSIEX01 subtitle: Duration of Treatment --- ------------------------------------------------------------------------ {{< 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: tsiex01.R # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: # - Define output ID and file location tblid <- "TSIEX01" fileid <- tblid tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map trtvar <- "TRT01A" popfl <- "SAFFL" ctrl_grp <- "Placebo" comp_btw_group <- TRUE ancova <- TRUE trtdur <- "TRTDURM" daysconv <- 30.4375 catlevels <- c( "0 to <3 months", "3 to <6 months", "6 to <9 months", "9 to <12 months", "12 to <15 months", "15 to <18 months", "18 to <21 months", "21 to <24 months", "24 to <27 months", "27 to <30 months", "30 to <33 months", "33 to <36 months", "36 to <39 months" ) 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 == trtdur & !is.na(AVAL) & VISIT == "Overall") %>% mutate( CRIT0FL = "Y", CRIT0 = as.factor("Any duration (at least 1 dose)") ) %>% select(USUBJID, PARAMCD, AVAL, AVALCAT1, starts_with("CRIT")) adexsum$CRIT0FL <- factor(adexsum$CRIT0FL, levels = c("Y", "N")) adsl$colspan_trt <- factor( ifelse(adsl[[trtvar]] == "Placebo", " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) adsl$diff_header <- "Difference in Means (95% CI)" adsl$diff_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp)) # join data together ex <- adexsum %>% inner_join(., adsl, by = c("USUBJID")) # Keep only columns with some data in which will remove any unwanted CRITy variables ex <- ex[, colSums(is.na(ex)) < nrow(ex)] # Work out how many CRITy vars (ignoring CRIT0 we created) we have left excritvars <- ex %>% select(num_range("CRIT", 1:99)) countcritvars <- length(names(excritvars)) # Drop unwanted levels for all CRITy variables you have remaining in ex and also for AVALCAT1 for (i in 1:countcritvars) { variable_name <- paste0("CRIT", i) ex[[variable_name]] <- droplevels(ex[[variable_name]]) } # drop unwanted levels from AVALCAT1 and assign levels from specified section at the top of script ex$AVALCAT1 <- droplevels(ex$AVALCAT1) ex$AVALCAT1 <- factor(ex$AVALCAT1, levels = catlevels) 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 ) ref_path <- c("colspan_trt", "", trtvar, ctrl_grp) # Create label_map to be utilized in the Criterion variables (CRIT) ex_lblmap <- ex %>% select(-CRIT0, -CRIT0FL) %>% tidyr::pivot_longer( cols = starts_with("CRIT") & ends_with("FL"), names_to = "value", values_to = "CF_value" ) %>% # Create the label by selecting the corresponding values from the CRIT columns mutate( label = case_when( value == "CRIT1FL" ~ CRIT1, value == "CRIT2FL" ~ CRIT2, value == "CRIT3FL" ~ CRIT3, value == "CRIT4FL" ~ CRIT4, value == "CRIT5FL" ~ CRIT5, value == "CRIT6FL" ~ CRIT6, value == "CRIT7FL" ~ CRIT7 ) ) %>% select(value, label) %>% distinct() # Define layout and build table: extra_args_rr <- list(.stats = c("count_unique_fraction"), riskdiff = FALSE) 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_cols_by("diff_header", nested = FALSE) %>% split_cols_by( trtvar, split_fun = remove_split_levels(ctrl_grp), labels_var = "diff_label" ) %>% analyze( "AVAL", afun = a_summarize_ex_j, var_labels = "Duration~[super a], ([unit])", #TODO FIX THIS show_labels = "visible", indent_mod = 0L, extra_args = list( daysconv = daysconv, ref_path = ref_path, ancova = ancova, comp_btw_group = comp_btw_group, variables = list(arm = trtvar, covariates = NULL), .formats = c( range = jjcsformat_xx("xx.x, xx.x"), "diff_mean_est_ci" = jjcsformat_xx("xx.xx (xx.xx, xx.xx)") ) ) ) %>% analyze( "AVALCAT1", var_labels = "Duration of treatment, n (%)", afun = a_freq_j, extra_args = extra_args_rr, indent_mod = 1L, show_labels = "visible", nested = FALSE ) %>% analyze( paste0("CRIT", 1, "FL"), var_labels = "Duration~[super b], n (%)", #TODO FIX THIS afun = a_freq_j, extra_args = append( extra_args_rr, list(label = as.character(ex_lblmap$label[1]), val = "Y") ), indent_mod = 1L, show_labels = "visible", nested = FALSE ) # Add in analyze for all others CRIT variables contained in ex for (i in 2:countcritvars) { lyt <- lyt %>% analyze( paste0("CRIT", i, "FL"), afun = a_freq_j, extra_args = append( extra_args_rr, list(label = as.character(ex_lblmap$label[i]), val = "Y") ), indent_mod = 2L, show_labels = "hidden" ) } lyt <- lyt %>% append_topleft("Parameter") result <- build_table(lyt, ex, alt_counts_df = adsl) ## Remove the N=xx column headers for the Difference (95% CI) columns result <- remove_col_count(result, span_label_var = "diff_header") # 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')`) ::::