TSFECG05

Subjects With ECG Values Outside Specified Limits Based on On-treatment Value and Over Time

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# Program Name:              tsfecg05

# Prep Environment

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

# Define script level parameters:

tblid <- "TSFECG05"
fileid <- tblid
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

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

ad_domain <- "adeg"

selvisit <- c(
  "Month 1",
  "Month 3",
  "Month 6",
  "Month 9",
  "Month 12",
  "Month 15",
  "Month 18",
  "Month 24"
)

selvisit <- c("Month 1", "Month 3")

catvar <- c("CRIT1", "CRIT2")

# initial read of data

adeg_complete <- pharmaverseadamjnj::adeg

## check parameters that have CRIT1,CRIT2 defined in data
levels_data <- unique(adeg_complete %>% select(PARAMCD, PARAM, CRIT1, CRIT2))

# restrict to these
selparamcd <- as.character(unique(
  levels_data %>% filter(!(is.na(CRIT1) | is.na(CRIT2))) %>% pull(PARAMCD)
))

## if the option TRTEMFL needs to be added to the TLF
trtemfl <- TRUE

# Mapping for CRIT1/2

xlabel_map <- levels_data %>%
  tidyr::pivot_longer(
    cols = c("CRIT1", "CRIT2"),
    names_to = "var",
    values_to = "label"
  ) %>%
  filter(!is.na(label)) %>%
  mutate(
    label = as.character(label),
    var = paste0(var, "FL"),
    value = "Y"
  )

#### Note: this is not in line with the markedly abnormal file
## for both EGHRMN & PRAG: crit1 and crit2 are reversed
## crit1 and crit2 on data are consistent with the order specified in the shell : low/high

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(.data[[popfl]] == "Y") %>%
  select(USUBJID, all_of(c(trtvar, popfl)))

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

adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))


adeg <- adeg_complete %>%
  filter(PARAMCD %in% selparamcd) %>%
  select(
    USUBJID,
    PARAM,
    PARAMCD,
    AVISIT,
    AVAL,
    CRIT1,
    CRIT1FL,
    CRIT2,
    CRIT2FL,
    starts_with("ANL") & ends_with("FL"),
    ONTRTFL,
    TRTEMFL,
    APOBLFL
  ) %>%
  inner_join(., adsl)

# filter data

### alert on trtemfl, do not apply it as a filter, as this would lead to incorrect denominators

adeg_crit_any <- unique(
  adeg %>%
    # synthetic data is missing the ANL04FL variable, however, this wouldn't be sufficient either, unless it flags both CRIT1 and CRIT2
    filter(ONTRTFL == "Y") %>%
    mutate(AVISIT = factor("On-treatment")) %>%
    select(-c(AVAL, ANL01FL, ANL02FL, ANL03FL))
)

### note: this does not necesarily leads to one record per parameter per subject
### if ANL04FL is available, we still can end up with more than one record
### a subject can have crit1fl=Y at one visit, and crit2fl=Y at another visit, and crit1fl=N&crit2fl=N at another
### as long as the analysis function deals with multiple records per subject correctly, this is not an issue

check_dup_sub <- adeg_crit_any %>%
  group_by(USUBJID, PARAMCD, AVISIT) %>%
  mutate(n_rec = n()) %>%
  filter(n_rec > 1)

# Over time is also restricted to on treatment value
adeg_crit_overtime <- adeg %>%
  filter(ANL02FL == "Y" & AVISIT %in% selvisit & ONTRTFL == "Y") %>%
  select(-c(AVAL, ANL01FL, ANL02FL, ANL03FL))

adeg_crit_comb <- rbind(adeg_crit_any, adeg_crit_overtime) %>%
  mutate(
    AVISIT = factor(
      as.character(AVISIT),
      levels = c("On-treatment", levels(adeg_crit_overtime$AVISIT))
    )
  ) %>%
  inner_join(., adsl)


#### DO NOT USE TRTEMFL = Y in filter, as this will remove subjects from both numerator and denominator
#### instead : set "CRIT2FL","CRIT1FL" to a non-reportable value (ie N) and keep in dataset
if (trtemfl) {
  adeg_crit_comb <- adeg_crit_comb %>%
    mutate(
      CRIT1FL = case_when(
        is.na(TRTEMFL) | TRTEMFL != "Y" ~ "N",
        TRUE ~ CRIT1FL
      ),
      CRIT2FL = case_when(
        is.na(TRTEMFL) | TRTEMFL != "Y" ~ "N",
        TRUE ~ CRIT2FL
      )
    )
}

adeg_crit_comb <- adeg_crit_comb %>%
  mutate(
    CRIT1FL = factor(CRIT1FL, levels = c("Y", "N")),
    CRIT2FL = factor(CRIT2FL, levels = c("Y", "N"))
  )

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:

extra_args_rr1 <- list(method = "wald", denom = "n_df", .stats = c("n_df"))
extra_args_rr2 <- list(
  method = "wald",
  denom = "n_df",
  .stats = c("count_unique_fraction")
)

lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx") %>%
  split_cols_by(
    "colspan_trt",
    split_fun = trim_levels_to_map(map = colspan_trt_map)
  ) %>%
  split_cols_by(
    trtvar
    # , split_fun = add_combo_levels(combodf)
  ) %>%
  # re-enable the below if relative risk columns are wanted
  # split_cols_by("rrisk_header", nested = FALSE) %>%
  # split_cols_by(trtvar, labels_var = "rrisk_label",
  #               split_fun = remove_split_levels(ctrl_grp)) %>%
  #
  split_rows_by(
    "PARAM",
    split_label = "Parameter",
    label_pos = "topleft",
    split_fun = drop_split_levels
  ) %>%
  split_rows_by(
    "AVISIT",
    split_label = "Study Visit",
    label_pos = "topleft",
    section_div = " ",
    split_fun = drop_split_levels
  ) %>%
  analyze(
    c("CRIT1"),
    a_freq_j,
    extra_args = extra_args_rr1,
    show_labels = "hidden",
    indent_mod = 0L
  ) %>%
  # denominators are varying per test, no need to show as N is shown in line above
  # revise order to first present low then high
  analyze(
    c("CRIT2FL", "CRIT1FL"),
    a_freq_j,
    extra_args = append(
      extra_args_rr2,
      list(
        val = c("Y"),
        label_map = xlabel_map
      )
    ),
    show_labels = "hidden",
    indent_mod = 1L
  ) %>%
  append_topleft("    Criteria, n (%)")

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

# Add titles and footnotes:

result <- set_titles(result, titles)

# Convert to tbl file and output table

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

TSFECG05: Subjects With ECG Values Outside Specified Limits Based on On-treatment Value and Over Time; Safety Analysis Set (Study jjcs - core)
Parameter	Active Study Agent
Study Visit	Xanomeline High Dose	Xanomeline Low Dose	Placebo
Criteria, n (%)	N=53	N=73	N=59
ECG Mean Heart Rate (beats/min)
On-treatment
N	53	71	58
>100	53 (100.0%)	67 (100.0%)	57 (100.0%)
<50	25 (100.0%)	22 (100.0%)	24 (100.0%)

Month 1
N	53	71	58
>100	36 (100.0%)	57 (100.0%)	45 (100.0%)
<50	11 (100.0%)	6 (100.0%)	8 (100.0%)

Month 3
N	53	53	56
>100	42 (100.0%)	43 (100.0%)	45 (100.0%)
<50	7 (100.0%)	7 (100.0%)	3 (100.0%)

PR Interval, Aggregate (msec)
On-treatment
N	53	71	58
>200	53 (100.0%)	67 (100.0%)	54 (100.0%)
<120	34 (100.0%)	43 (100.0%)	47 (100.0%)

Month 1
N	53	71	58
>200	39 (100.0%)	46 (100.0%)	35 (100.0%)
<120	6 (100.0%)	17 (100.0%)	13 (100.0%)

Month 3
N	53	53	56
>200	34 (100.0%)	33 (100.0%)	33 (100.0%)
<120	9 (100.0%)	10 (100.0%)	14 (100.0%)

Note: On-treatment is defined as treatment-emergentECG values obtained after the first dose and within [30 days] following treatment discontinuation. [Treatment-emergent values are those that worsened from baseline.]
Note: Specified limits are defined in the Statistical Analysis Plan. N is the number of subjects with at least 1 postbaseline value for the specified ECG parameter.

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--- title: TSFECG05 subtitle: Subjects With ECG Values Outside Specified Limits Based on On-treatment Value and Over Time --- ------------------------------------------------------------------------ {{< 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: tsfecg05 # Prep Environment library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: tblid <- "TSFECG05" fileid <- tblid titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map popfl <- "SAFFL" trtvar <- "TRT01A" ctrl_grp <- "Placebo" ad_domain <- "adeg" selvisit <- c( "Month 1", "Month 3", "Month 6", "Month 9", "Month 12", "Month 15", "Month 18", "Month 24" ) selvisit <- c("Month 1", "Month 3") catvar <- c("CRIT1", "CRIT2") # initial read of data adeg_complete <- pharmaverseadamjnj::adeg ## check parameters that have CRIT1,CRIT2 defined in data levels_data <- unique(adeg_complete %>% select(PARAMCD, PARAM, CRIT1, CRIT2)) # restrict to these selparamcd <- as.character(unique( levels_data %>% filter(!(is.na(CRIT1) | is.na(CRIT2))) %>% pull(PARAMCD) )) ## if the option TRTEMFL needs to be added to the TLF trtemfl <- TRUE # Mapping for CRIT1/2 xlabel_map <- levels_data %>% tidyr::pivot_longer( cols = c("CRIT1", "CRIT2"), names_to = "var", values_to = "label" ) %>% filter(!is.na(label)) %>% mutate( label = as.character(label), var = paste0(var, "FL"), value = "Y" ) #### Note: this is not in line with the markedly abnormal file ## for both EGHRMN & PRAG: crit1 and crit2 are reversed ## crit1 and crit2 on data are consistent with the order specified in the shell : low/high # Process Data: adsl <- pharmaverseadamjnj::adsl %>% filter(.data[[popfl]] == "Y") %>% select(USUBJID, all_of(c(trtvar, popfl))) adsl$colspan_trt <- factor( ifelse(adsl[[trtvar]] == ctrl_grp, " ", "Active Study Agent"), levels = c("Active Study Agent", " ") ) adsl$rrisk_header <- "Risk Difference (%) (95% CI)" adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp)) adeg <- adeg_complete %>% filter(PARAMCD %in% selparamcd) %>% select( USUBJID, PARAM, PARAMCD, AVISIT, AVAL, CRIT1, CRIT1FL, CRIT2, CRIT2FL, starts_with("ANL") & ends_with("FL"), ONTRTFL, TRTEMFL, APOBLFL ) %>% inner_join(., adsl) # filter data ### alert on trtemfl, do not apply it as a filter, as this would lead to incorrect denominators adeg_crit_any <- unique( adeg %>% # synthetic data is missing the ANL04FL variable, however, this wouldn't be sufficient either, unless it flags both CRIT1 and CRIT2 filter(ONTRTFL == "Y") %>% mutate(AVISIT = factor("On-treatment")) %>% select(-c(AVAL, ANL01FL, ANL02FL, ANL03FL)) ) ### note: this does not necesarily leads to one record per parameter per subject ### if ANL04FL is available, we still can end up with more than one record ### a subject can have crit1fl=Y at one visit, and crit2fl=Y at another visit, and crit1fl=N&crit2fl=N at another ### as long as the analysis function deals with multiple records per subject correctly, this is not an issue check_dup_sub <- adeg_crit_any %>% group_by(USUBJID, PARAMCD, AVISIT) %>% mutate(n_rec = n()) %>% filter(n_rec > 1) # Over time is also restricted to on treatment value adeg_crit_overtime <- adeg %>% filter(ANL02FL == "Y" & AVISIT %in% selvisit & ONTRTFL == "Y") %>% select(-c(AVAL, ANL01FL, ANL02FL, ANL03FL)) adeg_crit_comb <- rbind(adeg_crit_any, adeg_crit_overtime) %>% mutate( AVISIT = factor( as.character(AVISIT), levels = c("On-treatment", levels(adeg_crit_overtime$AVISIT)) ) ) %>% inner_join(., adsl) #### DO NOT USE TRTEMFL = Y in filter, as this will remove subjects from both numerator and denominator #### instead : set "CRIT2FL","CRIT1FL" to a non-reportable value (ie N) and keep in dataset if (trtemfl) { adeg_crit_comb <- adeg_crit_comb %>% mutate( CRIT1FL = case_when( is.na(TRTEMFL) | TRTEMFL != "Y" ~ "N", TRUE ~ CRIT1FL ), CRIT2FL = case_when( is.na(TRTEMFL) | TRTEMFL != "Y" ~ "N", TRUE ~ CRIT2FL ) ) } adeg_crit_comb <- adeg_crit_comb %>% mutate( CRIT1FL = factor(CRIT1FL, levels = c("Y", "N")), CRIT2FL = factor(CRIT2FL, levels = c("Y", "N")) ) 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: extra_args_rr1 <- list(method = "wald", denom = "n_df", .stats = c("n_df")) extra_args_rr2 <- list( method = "wald", denom = "n_df", .stats = c("count_unique_fraction") ) lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx") %>% split_cols_by( "colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map) ) %>% split_cols_by( trtvar # , split_fun = add_combo_levels(combodf) ) %>% # re-enable the below if relative risk columns are wanted # split_cols_by("rrisk_header", nested = FALSE) %>% # split_cols_by(trtvar, labels_var = "rrisk_label", # split_fun = remove_split_levels(ctrl_grp)) %>% # split_rows_by( "PARAM", split_label = "Parameter", label_pos = "topleft", split_fun = drop_split_levels ) %>% split_rows_by( "AVISIT", split_label = "Study Visit", label_pos = "topleft", section_div = " ", split_fun = drop_split_levels ) %>% analyze( c("CRIT1"), a_freq_j, extra_args = extra_args_rr1, show_labels = "hidden", indent_mod = 0L ) %>% # denominators are varying per test, no need to show as N is shown in line above # revise order to first present low then high analyze( c("CRIT2FL", "CRIT1FL"), a_freq_j, extra_args = append( extra_args_rr2, list( val = c("Y"), label_map = xlabel_map ) ), show_labels = "hidden", indent_mod = 1L ) %>% append_topleft(" Criteria, n (%)") result <- build_table(lyt, adeg_crit_comb, alt_counts_df = adsl) # Add titles and footnotes: result <- set_titles(result, titles) # Convert to tbl file and output table tt_to_tlgrtf(string_map = string_map, tt = result, file = fileid) ``` ```{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')`) ::::