TSFVIT02

Subjects With Maximum On-treatment Systolic Blood Pressure by Category of Blood Pressure

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# Program Name:              tsfvit02

# Prep environment:

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

# Define script level parameters:

tblid <- "TSFVIT02"
fileid <- tblid
titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

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

selparamcd <- c("SYSBP", "DIABP")

### tsfvit02a
selparamcd <- c("SYSBP")

### tsfvit02b : no code updates needed
# selparamcd <- c("DIABP")

### as in dataset, order is important for later processing
### not automated, hard coded approach for ease of reading
### ideally the datasets already contain the appropriate case, to ensure units are in proper case
sel_param <- c("Systolic Blood Pressure (mmHg)")
sel_param_case <- c("Systolic blood pressure (mmHg)")

# Process Data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(.data[[popfl]] == "Y") %>%
  select(
    USUBJID,
    all_of(c(popfl, trtvar)),
    SEX_DECODE,
    AGEGR1,
    RACE_DECODE,
    ETHNIC_DECODE
  )

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))

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
)

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)

### note: by filter ANL06FL, this table is restricted to On-treatment values, per definition of ANL06FL
### therefor, no need to add ONTRTFL in filter
### if derivation of ANL06FL 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 !!!
filtered_advs <- pharmaverseadamjnj::advs %>%
  filter(PARAMCD %in% selparamcd) %>%
  filter(ANL06FL == "Y") %>%
  select(
    STUDYID,
    USUBJID,
    PARAMCD,
    PARAM,
    AVALCAT1,
    AVALCA1N,
    AVISIT,
    ANL06FL,
    APOBLFL,
    ONTRTFL
  ) %>%
  inner_join(adsl)

filtered_advs$PARAM <- factor(
  as.character(filtered_advs$PARAM),
  levels = sel_param,
  labels = sel_param_case
)

# Process markedly abnormal values from spreadsheet:

### Markedly Abnormal spreadsheet

markedlyabnormal_file <- file.path('../../_data', "markedlyabnormal.xlsx")


markedlyabnormal_sheets <- readxl::excel_sheets(markedlyabnormal_file)

lbmarkedlyabnormal_defs <- readxl::read_excel(
  markedlyabnormal_file,
  sheet = "ADVS"
) %>%
  filter(PARAMCD != "Parameter Code") %>%
  arrange(PARAMCD, VARNAME, ORDER) %>%
  filter(VARNAME == "AVALCAT1") %>%
  filter(PARAMCD %in% selparamcd) %>%
  left_join(., unique(filtered_advs %>% select(PARAMCD, PARAM)))


# code_decode <- getcodelistinfo(df=filtered_advs,
#                                domain="advs",
#                                vars = c("AVALCAT1"),
#                                APT= apt,
#                                adam_meta_loc=am_in,
#                                sdtm_meta_loc=dm_in)
#
#
# code_decode <- code_decode %>%
#   filter(PARAMCD %in% selparamcd) %>%
#   left_join(.,unique(filtered_advs %>% select(PARAMCD,PARAM)))

### create a mapping table for usage in split_fun trim_levels_to_map,
### to ensure only the levels appropriate for the selected parameters are covered

param_map <- lbmarkedlyabnormal_defs %>%
  rename(AVALCAT1 = CRIT) %>%
  ### no factors are allowed in this split_fun map definition
  mutate(PARAM = as.character(PARAM)) %>%
  select(PARAM, AVALCAT1)

# if no code list was defined, not all defined categories might be present on data
# sas2rds conversion might not have added all categories as factor levels - need to do this here
if (
  class(filtered_advs$AVALCAT1) == "character" ||
    !all(unique(param_map$AVALCAT1) %in% levels(filtered_advs$AVALCAT1))
) {
  filtered_advs$AVALCAT1 <- factor(
    as.character(filtered_advs$AVALCAT1),
    levels = unique(param_map$AVALCAT1)
  )
}

# filtered_advs$PARAM <- droplevels(filtered_advs$PARAM)

filtered_advs <- var_relabel(filtered_advs, PARAM = "Blood Pressure")

# Define layout and build table:

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

lyt0 <- 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_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",
    label_pos = "topleft",
    child_labels = "hidden",
    split_label = "Systolic Blood Pressure (mmHg), n (%)",
    section_div = " ",
    ## ensure only the appropriate levels inside PARAM-AVALCAT1 will be included
    split_fun = trim_levels_to_map(param_map)
  )

# version without explicit denominator (as in shell)
lyt <- lyt0 %>%
  # for testing, it is sometimes convenient to explicitely show the used denominator
  analyze(
    "AVALCAT1",
    a_freq_j,
    extra_args = extra_args_rr,
    show_labels = "hidden",
    indent_mod = 0L
  )


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

# Post-Processing:

result <- remove_col_count(result)

# 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, orientation = "landscape")

TSFVIT02: Subjects With Maximum On-treatment Systolic Blood Pressure by Category of Blood Pressure; Safety Analysis Set (Study jjcs - core)
	Active Study Agent			Risk Difference (%) (95% CI)
	Xanomeline High Dose	Xanomeline Low Dose	Placebo	Xanomeline High Dose vs Placebo	Xanomeline Low Dose vs Placebo
Systolic Blood Pressure (mmHg), n (%)	N=53	N=73	N=59
N	49	69	55
<90	0	0	4 (7.3%)	-7.3 (-14.1, -0.4)	-7.3 (-14.1, -0.4)
≥90 to 119	27 (55.1%)	23 (33.3%)	27 (49.1%)	6.0 (-13.2, 25.2)	-15.8 (-33.0, 1.5)
≥120 to 139	17 (34.7%)	30 (43.5%)	18 (32.7%)	2.0 (-16.2, 20.2)	10.8 (-6.3, 27.8)
≥140 to 159	5 (10.2%)	12 (17.4%)	6 (10.9%)	-0.7 (-12.5, 11.1)	6.5 (-5.7, 18.6)
≥160 to 179	0	4 (5.8%)	0	0.0 (0.0, 0.0)	5.8 (0.3, 11.3)
≥180	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)

Note: On-treatment is defined as blood pressure values obtained after the first dose and within [30 days] following treatment discontinuation.

Download RTF file

--- title: TSFVIT02 subtitle: Subjects With Maximum On-treatment Systolic Blood Pressure by Category of Blood Pressure --- ------------------------------------------------------------------------ {{< 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: tsfvit02 # Prep environment: library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: tblid <- "TSFVIT02" fileid <- tblid titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map popfl <- "SAFFL" trtvar <- "TRT01A" ctrl_grp <- "Placebo" selparamcd <- c("SYSBP", "DIABP") ### tsfvit02a selparamcd <- c("SYSBP") ### tsfvit02b : no code updates needed # selparamcd <- c("DIABP") ### as in dataset, order is important for later processing ### not automated, hard coded approach for ease of reading ### ideally the datasets already contain the appropriate case, to ensure units are in proper case sel_param <- c("Systolic Blood Pressure (mmHg)") sel_param_case <- c("Systolic blood pressure (mmHg)") # Process Data: adsl <- pharmaverseadamjnj::adsl %>% filter(.data[[popfl]] == "Y") %>% select( USUBJID, all_of(c(popfl, trtvar)), SEX_DECODE, AGEGR1, RACE_DECODE, ETHNIC_DECODE ) 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$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp)) colspan_trt_map <- create_colspan_map( adsl, non_active_grp = ctrl_grp, non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) colspan_trt_map <- create_colspan_map( adsl, non_active_grp = ctrl_grp, non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp) ### note: by filter ANL06FL, this table is restricted to On-treatment values, per definition of ANL06FL ### therefor, no need to add ONTRTFL in filter ### if derivation of ANL06FL is not restricted to ONTRTFL records, adding ONTRTFL here will not give the correct answer either ### as mixing worst with other period is not giving the proper selection !!! filtered_advs <- pharmaverseadamjnj::advs %>% filter(PARAMCD %in% selparamcd) %>% filter(ANL06FL == "Y") %>% select( STUDYID, USUBJID, PARAMCD, PARAM, AVALCAT1, AVALCA1N, AVISIT, ANL06FL, APOBLFL, ONTRTFL ) %>% inner_join(adsl) filtered_advs$PARAM <- factor( as.character(filtered_advs$PARAM), levels = sel_param, labels = sel_param_case ) # Process markedly abnormal values from spreadsheet: ### Markedly Abnormal spreadsheet markedlyabnormal_file <- file.path('../../_data', "markedlyabnormal.xlsx") markedlyabnormal_sheets <- readxl::excel_sheets(markedlyabnormal_file) lbmarkedlyabnormal_defs <- readxl::read_excel( markedlyabnormal_file, sheet = "ADVS" ) %>% filter(PARAMCD != "Parameter Code") %>% arrange(PARAMCD, VARNAME, ORDER) %>% filter(VARNAME == "AVALCAT1") %>% filter(PARAMCD %in% selparamcd) %>% left_join(., unique(filtered_advs %>% select(PARAMCD, PARAM))) # code_decode <- getcodelistinfo(df=filtered_advs, # domain="advs", # vars = c("AVALCAT1"), # APT= apt, # adam_meta_loc=am_in, # sdtm_meta_loc=dm_in) # # # code_decode <- code_decode %>% # filter(PARAMCD %in% selparamcd) %>% # left_join(.,unique(filtered_advs %>% select(PARAMCD,PARAM))) ### create a mapping table for usage in split_fun trim_levels_to_map, ### to ensure only the levels appropriate for the selected parameters are covered param_map <- lbmarkedlyabnormal_defs %>% rename(AVALCAT1 = CRIT) %>% ### no factors are allowed in this split_fun map definition mutate(PARAM = as.character(PARAM)) %>% select(PARAM, AVALCAT1) # if no code list was defined, not all defined categories might be present on data # sas2rds conversion might not have added all categories as factor levels - need to do this here if ( class(filtered_advs$AVALCAT1) == "character" || !all(unique(param_map$AVALCAT1) %in% levels(filtered_advs$AVALCAT1)) ) { filtered_advs$AVALCAT1 <- factor( as.character(filtered_advs$AVALCAT1), levels = unique(param_map$AVALCAT1) ) } # filtered_advs$PARAM <- droplevels(filtered_advs$PARAM) filtered_advs <- var_relabel(filtered_advs, PARAM = "Blood Pressure") # Define layout and build table: extra_args_rr <- list( method = "wald", denom = "n_df", ref_path = ref_path, .stats = c("denom", "count_unique_fraction") ) lyt0 <- 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_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", label_pos = "topleft", child_labels = "hidden", split_label = "Systolic Blood Pressure (mmHg), n (%)", section_div = " ", ## ensure only the appropriate levels inside PARAM-AVALCAT1 will be included split_fun = trim_levels_to_map(param_map) ) # version without explicit denominator (as in shell) lyt <- lyt0 %>% # for testing, it is sometimes convenient to explicitely show the used denominator analyze( "AVALCAT1", a_freq_j, extra_args = extra_args_rr, show_labels = "hidden", indent_mod = 0L ) result <- build_table(lyt, filtered_advs, alt_counts_df = adsl) # Post-Processing: result <- remove_col_count(result) # 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, 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')`) ::::