TSFECG04

Shift From Baseline to Maximum On-treatment Corrected QT Interval

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# Program Name:              tsfecg04

# Prep Environment

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

# Define script level parameters:

tblid <- "TSFECG04"
fileid <- tblid
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

popfl <- "SAFFL"
trtvar <- "TRT01A"
ctrl_grp <- "Placebo"
demogvars <- c("SEX", "AGEGR1", "RACE", "ETHNIC", "AGE")

ad_domain <- "ADEG"

## selection of QTC parameters
selparamcd <- c("QTCFAG", "QTCBAG", "QTCS", "QTCLAG")

# initial read of data

adeg_complete <- pharmaverseadamjnj::adeg

### available QTC parameters in study
selparamcd <- intersect(selparamcd, unique(adeg_complete$PARAMCD))

catvar <- "AVALCAT1"

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(!!sym(popfl) == "Y") %>%
  select(STUDYID, USUBJID, all_of(c(trtvar, popfl, demogvars)))

adsl <- adsl %>%
  mutate(
    colspan_trt = factor(
      ifelse(!!sym(trtvar) == ctrl_grp, " ", "Active Study Agent"),
      levels = c("Active Study Agent", " ")
    )
  )
## to ensure the same order as on other outputs
trt_order <- as.character((unique(
  adsl %>% select("colspan_trt", all_of(trtvar))
) %>%
  arrange(colspan_trt, !!sym(trtvar)))[[trtvar]])
adsl[[trtvar]] <- factor(as.character(adsl[[trtvar]]), levels = trt_order)

adeg <- adeg_complete %>%
  filter(PARAMCD %in% selparamcd) %>%
  ### Maximum On-treatment
  ### note: by filter ANL03FL, this table is restricted to On-treatment values, per definition of ANL03FL
  ### therefor, no need to add ONTRTFL in filter
  ### if derivation of ANL03FL is not restricted to ONTRTFL records, adding ONTRTFL here will not give the correct answer either
  ### as mixing worst with other period is not giving the proper selection !!!
  filter(ANL03FL == "Y") %>%
  select(
    USUBJID,
    ONTRTFL,
    TRTEMFL,
    PARAM,
    PARAMCD,
    AVISITN,
    AVISIT,
    AVAL,
    BASE,
    CHG,
    AVALCAT1,
    BASECAT1,
    ONTRTFL,
    TRTEMFL,
    ANL01FL,
    ANL02FL,
    ANL03FL
  ) %>%
  inner_join(., adsl)

adeg$AVISIT <- factor("Maximum Corrected QT Interval")

## if also over time is needed, append these to above dataset
## see ecg05 as example

check1 <- adeg %>%
  group_by(TRT01A, PARAMCD, AVISIT) %>%
  summarize(n = n_distinct(USUBJID))


## add variable for column split header
adeg$BASECAT1_header <- "Baseline Corrected QT Interval"
adeg$BASECAT1_header2 <- " " ## first column N should not appear under Baseline column span

adeg$BASECAT1_header3 <- " " ## extra to allow for additional topleft material

###
AVALCAT1_levels <- levels(adeg$AVALCAT1)


## add extra level N to Basecat1
adeg <- adeg %>%
  mutate(
    BASECAT1 = factor(as.character(BASECAT1), levels = c("N", AVALCAT1_levels))
  )


## trick for alt_counts_df to work with col splitting
# add BASECAT1 to adsl, all assign to extra level N (column will be used for N counts)
adslx <- adsl %>%
  mutate(BASECAT1 = "N") %>%
  mutate(BASECAT1 = factor(BASECAT1, levels = c("N", AVALCAT1_levels)))


adslx$BASECAT1_header <- "Baseline Corrected QT Interval"
adslx$BASECAT1_header2 <- " "
adslx$BASECAT1_header3 <- " " ## extra to allow for additional topleft material

# Define layout and build table:

lyt <- basic_table(show_colcounts = FALSE) %>%
  split_cols_by("BASECAT1_header3") %>%
  ## to ensure N column is not under the Baseline column span header
  split_cols_by("BASECAT1_header2") %>%
  split_cols_by("BASECAT1", split_fun = keep_split_levels("N")) %>%
  ## restart column split (Nested = False)
  ## Combined levels will be made, and the N column should not appear
  split_cols_by("BASECAT1_header", nested = FALSE) %>%
  split_cols_by(
    "BASECAT1",
    split_fun = make_split_fun(
      pre = list(rm_levels(excl = "N")),
      post = list(
        add_overall_facet("TOTAL", "Total")
      )
    )
  ) %>%
  #### replace split_rows and summarize by single analyze call
  ### a_freq_j only works due to
  ### special arguments can do the trick : denomf = adslx & .stats = count_unique
  ### we want counts of treatment group coming from adsl, not from input dataset, therefor, countsource = altdf
  analyze(
    vars = trtvar,
    afun = a_freq_j,
    extra_args = list(
      restr_columns = "N",
      .stats = "count_unique",
      countsource = "altdf",
      extrablankline = TRUE
    ),
    indent_mod = -1L
  ) %>%
  # ## main part of table
  split_rows_by(
    "PARAM",
    nested = FALSE,
    label_pos = "topleft",
    child_labels = "visible",
    split_label = "QTc Interval",
    split_fun = drop_split_levels,
    section_div = " "
  ) %>%
  split_rows_by(
    trtvar,
    label_pos = "topleft",
    indent_mod = -1L,
    child_labels = "hidden",
    split_label = "Treatment Group",
    section_div = " "
  ) %>%
  ### a_freq_j
  ### the special statistic "n_rowdf" option does the trick here of getting the proper value for the N column
  summarize_row_groups(
    trtvar,
    cfun = a_freq_j,
    extra_args = list(
      .stats = "n_rowdf",
      restr_columns = c("N")
    )
  ) %>%
  split_rows_by(
    "AVISIT",
    label_pos = "hidden",
    indent_mod = 1L,
    split_label = " ",
    child_labels = "visible",
    section_div = " "
  ) %>%
  ## add extra level TOTAL using new_levels, rather than earlier technique
  ## advantage for denominator derivation -- n_rowdf can be used, if we'd like to present fraction as well
  ## switch .stats to count_unique_denom_fraction or count_unique_fraction
  analyze(
    "AVALCAT1",
    afun = a_freq_j,
    extra_args = list(
      .stats = "count_unique",
      denom = "n_rowdf",
      new_levels = list(c("Total"), list(AVALCAT1_levels)),
      new_levels_after = TRUE,
      .indent_mods = 1L,
      restr_columns = c(
        toupper(AVALCAT1_levels),
        "TOTAL"
      )
    )
  ) %>%
  append_topleft("    Criteria, n")

result <- build_table(lyt, adeg, alt_counts_df = adslx)

# Add titles and footnotes:

result <- set_titles(result, titles)

# Convert to tbl file and output table

# the default column-widths had issues with 2 columns appear too close
# retrieve the default column widths and update the latter columns
fontspec <- font_spec("Times", 9L, 1.2)
col_gap <- 7L
label_width_ins <- 2

colwidths <- def_colwidths(
  result,
  fontspec,
  col_gap = col_gap,
  label_width_ins = label_width_ins
)

# adjust the column-widths to have the same length for columns 3 - 7 (<= 450, ...., Total)
acolwidths <- colwidths
acolwidths[3:length(acolwidths)] <- 12

tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, colwidths = acolwidths)

TSFECG04: Shift From Baseline to Maximum On-treatment Corrected QT Interval; Safety Analysis Set (Study jjcs - core)
QTc Interval
Treatment Group		Baseline Corrected QT Interval
Criteria, n	N	≤450	>450 to ≤480	>480 to ≤500	>500	Total
Xanomeline High Dose	53
Xanomeline Low Dose	73
Placebo	59

QTcB Interval, Aggregate (msec)
Xanomeline High Dose	53
Maximum Corrected QT Interval
≤450		31	2	3	5	41
>450 to ≤480		4	0	2	0	6
>480 to ≤500		1	0	0	1	2
>500		1	2	0	1	4
Total		37	4	5	7	53

Xanomeline Low Dose	53
Maximum Corrected QT Interval
≤450		31	3	2	2	38
>450 to ≤480		2	1	0	1	4
>480 to ≤500		4	1	0	1	6
>500		5	0	0	0	5
Total		42	5	2	4	53

Placebo	56
Maximum Corrected QT Interval
≤450		40	4	1	2	47
>450 to ≤480		2	0	1	1	4
>480 to ≤500		2	0	0	0	2
>500		0	0	2	1	3
Total		44	4	4	4	56

QTcF Interval, Aggregate (msec)
Xanomeline High Dose	53
Maximum Corrected QT Interval
≤450		33	2	4	3	42
>450 to ≤480		3	0	0	0	3
>480 to ≤500		6	0	0	0	6
>500		2	0	0	0	2
Total		44	2	4	3	53

Xanomeline Low Dose	53
Maximum Corrected QT Interval
≤450		34	2	0	4	40
>450 to ≤480		3	0	0	1	4
>480 to ≤500		4	0	0	0	4
>500		4	0	1	0	5
Total		45	2	1	5	53

Placebo	56
Maximum Corrected QT Interval
≤450		29	6	5	3	43
>450 to ≤480		2	0	0	0	2
>480 to ≤500		4	1	0	0	5
>500		5	1	0	0	6
Total		40	8	5	3	56

Note: On-treatment is defined as QTc interval values obtained after the first dose and within [30 days] following treatment discontinuation.
Note: N is the number of subjects with non-missing values for the ECG parameter at baseline and at least 1 postbaseline visit.

Download RTF file

--- title: TSFECG04 subtitle: Shift From Baseline to Maximum On-treatment Corrected QT Interval --- ------------------------------------------------------------------------ {{< 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: tsfecg04 # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) library(rlang) # Define script level parameters: tblid <- "TSFECG04" fileid <- tblid titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map popfl <- "SAFFL" trtvar <- "TRT01A" ctrl_grp <- "Placebo" demogvars <- c("SEX", "AGEGR1", "RACE", "ETHNIC", "AGE") ad_domain <- "ADEG" ## selection of QTC parameters selparamcd <- c("QTCFAG", "QTCBAG", "QTCS", "QTCLAG") # initial read of data adeg_complete <- pharmaverseadamjnj::adeg ### available QTC parameters in study selparamcd <- intersect(selparamcd, unique(adeg_complete$PARAMCD)) catvar <- "AVALCAT1" # Process Data: adsl <- pharmaverseadamjnj::adsl %>% filter(!!sym(popfl) == "Y") %>% select(STUDYID, USUBJID, all_of(c(trtvar, popfl, demogvars))) adsl <- adsl %>% mutate( colspan_trt = factor( ifelse(!!sym(trtvar) == ctrl_grp, " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) ) ## to ensure the same order as on other outputs trt_order <- as.character((unique( adsl %>% select("colspan_trt", all_of(trtvar)) ) %>% arrange(colspan_trt, !!sym(trtvar)))[[trtvar]]) adsl[[trtvar]] <- factor(as.character(adsl[[trtvar]]), levels = trt_order) adeg <- adeg_complete %>% filter(PARAMCD %in% selparamcd) %>% ### Maximum On-treatment ### note: by filter ANL03FL, this table is restricted to On-treatment values, per definition of ANL03FL ### therefor, no need to add ONTRTFL in filter ### if derivation of ANL03FL is not restricted to ONTRTFL records, adding ONTRTFL here will not give the correct answer either ### as mixing worst with other period is not giving the proper selection !!! filter(ANL03FL == "Y") %>% select( USUBJID, ONTRTFL, TRTEMFL, PARAM, PARAMCD, AVISITN, AVISIT, AVAL, BASE, CHG, AVALCAT1, BASECAT1, ONTRTFL, TRTEMFL, ANL01FL, ANL02FL, ANL03FL ) %>% inner_join(., adsl) adeg$AVISIT <- factor("Maximum Corrected QT Interval") ## if also over time is needed, append these to above dataset ## see ecg05 as example check1 <- adeg %>% group_by(TRT01A, PARAMCD, AVISIT) %>% summarize(n = n_distinct(USUBJID)) ## add variable for column split header adeg$BASECAT1_header <- "Baseline Corrected QT Interval" adeg$BASECAT1_header2 <- " " ## first column N should not appear under Baseline column span adeg$BASECAT1_header3 <- " " ## extra to allow for additional topleft material ### AVALCAT1_levels <- levels(adeg$AVALCAT1) ## add extra level N to Basecat1 adeg <- adeg %>% mutate( BASECAT1 = factor(as.character(BASECAT1), levels = c("N", AVALCAT1_levels)) ) ## trick for alt_counts_df to work with col splitting # add BASECAT1 to adsl, all assign to extra level N (column will be used for N counts) adslx <- adsl %>% mutate(BASECAT1 = "N") %>% mutate(BASECAT1 = factor(BASECAT1, levels = c("N", AVALCAT1_levels))) adslx$BASECAT1_header <- "Baseline Corrected QT Interval" adslx$BASECAT1_header2 <- " " adslx$BASECAT1_header3 <- " " ## extra to allow for additional topleft material # Define layout and build table: lyt <- basic_table(show_colcounts = FALSE) %>% split_cols_by("BASECAT1_header3") %>% ## to ensure N column is not under the Baseline column span header split_cols_by("BASECAT1_header2") %>% split_cols_by("BASECAT1", split_fun = keep_split_levels("N")) %>% ## restart column split (Nested = False) ## Combined levels will be made, and the N column should not appear split_cols_by("BASECAT1_header", nested = FALSE) %>% split_cols_by( "BASECAT1", split_fun = make_split_fun( pre = list(rm_levels(excl = "N")), post = list( add_overall_facet("TOTAL", "Total") ) ) ) %>% #### replace split_rows and summarize by single analyze call ### a_freq_j only works due to ### special arguments can do the trick : denomf = adslx & .stats = count_unique ### we want counts of treatment group coming from adsl, not from input dataset, therefor, countsource = altdf analyze( vars = trtvar, afun = a_freq_j, extra_args = list( restr_columns = "N", .stats = "count_unique", countsource = "altdf", extrablankline = TRUE ), indent_mod = -1L ) %>% # ## main part of table split_rows_by( "PARAM", nested = FALSE, label_pos = "topleft", child_labels = "visible", split_label = "QTc Interval", split_fun = drop_split_levels, section_div = " " ) %>% split_rows_by( trtvar, label_pos = "topleft", indent_mod = -1L, child_labels = "hidden", split_label = "Treatment Group", section_div = " " ) %>% ### a_freq_j ### the special statistic "n_rowdf" option does the trick here of getting the proper value for the N column summarize_row_groups( trtvar, cfun = a_freq_j, extra_args = list( .stats = "n_rowdf", restr_columns = c("N") ) ) %>% split_rows_by( "AVISIT", label_pos = "hidden", indent_mod = 1L, split_label = " ", child_labels = "visible", section_div = " " ) %>% ## add extra level TOTAL using new_levels, rather than earlier technique ## advantage for denominator derivation -- n_rowdf can be used, if we'd like to present fraction as well ## switch .stats to count_unique_denom_fraction or count_unique_fraction analyze( "AVALCAT1", afun = a_freq_j, extra_args = list( .stats = "count_unique", denom = "n_rowdf", new_levels = list(c("Total"), list(AVALCAT1_levels)), new_levels_after = TRUE, .indent_mods = 1L, restr_columns = c( toupper(AVALCAT1_levels), "TOTAL" ) ) ) %>% append_topleft(" Criteria, n") result <- build_table(lyt, adeg, alt_counts_df = adslx) # Add titles and footnotes: result <- set_titles(result, titles) # Convert to tbl file and output table # the default column-widths had issues with 2 columns appear too close # retrieve the default column widths and update the latter columns fontspec <- font_spec("Times", 9L, 1.2) col_gap <- 7L label_width_ins <- 2 colwidths <- def_colwidths( result, fontspec, col_gap = col_gap, label_width_ins = label_width_ins ) # adjust the column-widths to have the same length for columns 3 - 7 (<= 450, ...., Total) acolwidths <- colwidths acolwidths[3:length(acolwidths)] <- 12 tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid, colwidths = acolwidths) ``` ```{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')`) ::::