TSFAE01A

Overall Summary of Subjects With Treatment-emergent Adverse Events

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# Program Name:              tsfae01a

# Prep environment:

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

# Define script level parameters:

tblid <- "TSFAE01a"
fileid <- tblid
popfl <- "SAFFL"
trtvar <- "TRT01A"
tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid)
string_map <- default_str_map

# Process data:

adsl <- pharmaverseadamjnj::adsl %>%
  filter(!!rlang::sym(popfl) == "Y") %>%
  create_colspan_var(
    non_active_grp = "Placebo",
    non_active_grp_span_lbl = " ",
    active_grp_span_lbl = "Active Study Agent",
    colspan_var = "colspan_trt",
    trt_var = trtvar
  ) %>%
  mutate(
    rrisk_header = "Risk Difference (%) (95% CI)",
    rrisk_label = paste(!!rlang::sym(trtvar), "vs Placebo")
  ) %>%
  select(
    USUBJID,
    !!rlang::sym(popfl),
    !!rlang::sym(trtvar),
    colspan_trt,
    rrisk_header,
    rrisk_label
  )

adae <- pharmaverseadamjnj::adae %>%
  filter(TRTEMFL == "Y") %>%
  select(
    USUBJID,
    AESER,
    AESDTH,
    AESLIFE,
    AESHOSP,
    AESDISAB,
    AESCONG,
    AESMIE,
    AEACN_DECODE,
    AESEV
  ) %>%
  group_by(USUBJID) %>%
  mutate(maxsev = max(as.character(AESEV), na.rm = TRUE)) %>%
  ungroup() %>%
  mutate(maxsev = ifelse(is.na(maxsev), "Missing", maxsev)) %>%
  mutate(
    maxsev = factor(maxsev, levels = c("Mild", "Moderate", "Severe", "Missing"))
  )

adae <- inner_join(adae, adsl, by = c("USUBJID"))

# Define layout and build table:

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
)

# Check the levels of AEACN_DECODE

aeacn_levels <- levels(adae$AEACN_DECODE)
# Here we are not considering "Drug Withdrawn", "Dose Not Changed", "Not Applicable"
excl_aeacn_levels <- c("Drug Withdrawn", "Dose Not Changed", "Not Applicable")
dosemod_lvls <- aeacn_levels[!(aeacn_levels %in% excl_aeacn_levels)]


## rearrange levels for AEACN_DECODE

newsort_AEACN_DECODE <- unique(c(
  "Drug Interrupted",
  "Dose Reduced",
  "Dose Rate Reduced",
  "Dose Increased",
  "Unknown",
  aeacn_levels
))

adae$AEACN_DECODE <- forcats::fct_relevel(
  adae$AEACN_DECODE,
  newsort_AEACN_DECODE
)

## mapping table for label updates

dosemod_lblmap <- tibble(value = dosemod_lvls, label = dosemod_lvls) %>%
  mutate(
    label = case_when(
      value == "Dose Increased" ~ label,
      value == "Dose Reduced" ~ "Reduction of study treatment",
      value == "Drug Interrupted" ~ "Interruption of study treatment",
      TRUE ~ label
    )
  )


dosemod_spf <- make_combo_splitfun(
  nm = "modified",
  label = "AE leading to dose modification of study",
  levels = c(
    "Dose Reduced",
    "Dose Increased",
    "Drug Interrupted",
    "Dose Rate Reduced",
    "Unknown"
  )
)
aesevall_spf <- make_combo_splitfun(
  nm = "AESEV_ALL",
  label = "Any AE~[super a]",
  levels = NULL
)


rr_method <- "wald"
ref_path <- c("colspan_trt", " ", trtvar, "Placebo")
extra_args_rr <- list(
  method = rr_method,
  ref_path = ref_path,
  .stats = c("count_unique_fraction")
)

lyt <- basic_table(
  show_colcounts = TRUE,
  colcount_format = "N=xx",
  top_level_section_div = " "
) %>%
  append_topleft(c(" ", " ", "Event, n (%)")) %>%
  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("Placebo")
  ) %>%
  split_rows_by(
    "AESER",
    split_fun = keep_split_levels("Y"),
    section_div = " "
  ) %>%
  summarize_row_groups(
    "AESER",
    cfun = a_freq_j,
    extra_args = list(
      label = "SAE",
      method = rr_method,
      ref_path = ref_path,
      .stats = c("count_unique_fraction")
    )
  ) %>%
  analyze(
    "AESDTH",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(label = "With fatal outcome", val = "Y", NULL)
    )
  ) %>%
  analyze(
    "AESLIFE",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(label = "Life-threatening", val = "Y", NULL)
    )
  ) %>%
  analyze(
    "AESHOSP",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(label = "Requiring or prolonging hospitalization", val = "Y", NULL)
    )
  ) %>%
  analyze(
    "AESDISAB",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(
        label = "Resulting in persistent or significant disability/incapacity",
        val = "Y",
        NULL
      )
    )
  ) %>%
  analyze(
    "AESCONG",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(label = "Congenital anomaly or birth defect", val = "Y", NULL)
    )
  ) %>%
  analyze(
    "AESMIE",
    afun = a_freq_j,
    show_labels = "hidden",
    extra_args = append(extra_args_rr, list(label = "Other", val = "Y", NULL))
  ) %>%
  analyze(
    "AEACN_DECODE",
    afun = a_freq_j,
    nested = FALSE,
    extra_args = append(
      extra_args_rr,
      list(
        label = "AE leading to permanent discontinuation of study treatment",
        val = "Drug Withdrawn",
        NULL
      )
    )
  ) %>%
  split_rows_by("AEACN_DECODE", split_fun = dosemod_spf, section_div = " ") %>%
  summarize_row_groups(
    "AEACN_DECODE",
    cfun = a_freq_j,
    extra_args = list(
      label = "AE leading to dose modification of study treatment",
      method = rr_method,
      ref_path = ref_path,
      .stats = c("count_unique_fraction")
    )
  ) %>%
  analyze(
    "AEACN_DECODE",
    table_names = "AEACN_DECODE",
    a_freq_j,
    show_labels = "hidden",
    extra_args = append(
      extra_args_rr,
      list(
        excl_levels = excl_aeacn_levels,
        label_map = dosemod_lblmap,
        drop_levels = TRUE
      )
    )
  ) %>%
  split_rows_by("maxsev", split_fun = aesevall_spf) %>%
  summarize_row_groups(
    "maxsev",
    cfun = a_freq_j,
    extra_args = list(
      label = "Any AE~[super a]",
      method = rr_method,
      ref_path = ref_path,
      .stats = c("count_unique_fraction")
    )
  ) %>%
  analyze("maxsev", afun = a_freq_j, extra_args = append(extra_args_rr, NULL))


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

# Post-Processing:

result <- remove_col_count(result)
result <- safe_prune_table(
  result,
  prune_func = count_pruner(
    cat_exclude = c(
      "With fatal outcome",
      "Life-threatening",
      "Requiring or prolonging hospitalization",
      "Resulting in persistent or significant disability/incapacity",
      "Congenital anomaly or birth defect",
      "Other"
    )
  )
)

# 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")

TSFAE01a: Overall Summary of Subjects With Treatment-emergent Adverse Events; 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
Event, n (%)	N=53	N=73	N=59
SAE	47 (88.7%)	54 (74.0%)	37 (62.7%)	26.0 (11.0, 41.0)	11.3 (-4.7, 27.2)
With fatal outcome	0	1 (1.4%)	1 (1.7%)	-1.7 (-5.0, 1.6)	-0.3 (-4.6, 3.9)
Life-threatening	0	1 (1.4%)	1 (1.7%)	-1.7 (-5.0, 1.6)	-0.3 (-4.6, 3.9)
Requiring or prolonging hospitalization	2 (3.8%)	2 (2.7%)	3 (5.1%)	-1.3 (-8.9, 6.3)	-2.3 (-9.1, 4.4)
Resulting in persistent or significant disability/incapacity	0	1 (1.4%)	0	0.0 (0.0, 0.0)	1.4 (-1.3, 4.0)
Congenital anomaly or birth defect	0	0	0	0.0 (0.0, 0.0)	0.0 (0.0, 0.0)
Other	47 (88.7%)	54 (74.0%)	37 (62.7%)	26.0 (11.0, 41.0)	11.3 (-4.7, 27.2)

AE leading to permanent discontinuation of study treatment	23 (43.4%)	20 (27.4%)	18 (30.5%)	12.9 (-4.9, 30.7)	-3.1 (-18.7, 12.5)

AE leading to dose modification of study treatment	51 (96.2%)	56 (76.7%)	40 (67.8%)	28.4 (15.5, 41.4)	8.9 (-6.5, 24.3)
Interruption of study treatment	28 (52.8%)	35 (47.9%)	19 (32.2%)	20.6 (2.7, 38.6)	15.7 (-0.8, 32.3)
Reduction of study treatment	24 (45.3%)	26 (35.6%)	17 (28.8%)	16.5 (-1.2, 34.2)	6.8 (-9.1, 22.7)
Dose Rate Reduced	31 (58.5%)	27 (37.0%)	15 (25.4%)	33.1 (15.8, 50.4)	11.6 (-4.1, 27.2)
Dose Increased	27 (50.9%)	25 (34.2%)	18 (30.5%)	20.4 (2.6, 38.3)	3.7 (-12.3, 19.8)
Unknown	24 (45.3%)	31 (42.5%)	13 (22.0%)	23.2 (6.2, 40.3)	20.4 (4.9, 35.9)

Any AEa	51 (96.2%)	62 (84.9%)	42 (71.2%)	25.0 (12.4, 37.7)	13.7 (-0.4, 27.9)
Mild	17 (32.1%)	15 (20.5%)	23 (39.0%)	-6.9 (-24.6, 10.8)	-18.4 (-34.0, -2.9)
Moderate	31 (58.5%)	35 (47.9%)	16 (27.1%)	31.4 (13.9, 48.8)	20.8 (4.7, 37.0)
Severe	3 (5.7%)	12 (16.4%)	3 (5.1%)	0.6 (-7.8, 8.9)	11.4 (1.2, 21.5)

a Dose modifications as collected on the AE CRF are presented. Subjects may be counted once for each modification type.
b The event experienced by the subject with the worst severity is used.

Download RTF file

--- title: TSFAE01A subtitle: Overall Summary of Subjects With Treatment-emergent Adverse Events --- ------------------------------------------------------------------------ {{< 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: tsfae01a # Prep environment: library(envsetup) library(tern) library(dplyr) library(rtables) library(junco) # Define script level parameters: tblid <- "TSFAE01a" fileid <- tblid popfl <- "SAFFL" trtvar <- "TRT01A" tab_titles <- get_titles_from_file(input_path = '../../_data/', tblid) string_map <- default_str_map # Process data: adsl <- pharmaverseadamjnj::adsl %>% filter(!!rlang::sym(popfl) == "Y") %>% create_colspan_var( non_active_grp = "Placebo", non_active_grp_span_lbl = " ", active_grp_span_lbl = "Active Study Agent", colspan_var = "colspan_trt", trt_var = trtvar ) %>% mutate( rrisk_header = "Risk Difference (%) (95% CI)", rrisk_label = paste(!!rlang::sym(trtvar), "vs Placebo") ) %>% select( USUBJID, !!rlang::sym(popfl), !!rlang::sym(trtvar), colspan_trt, rrisk_header, rrisk_label ) adae <- pharmaverseadamjnj::adae %>% filter(TRTEMFL == "Y") %>% select( USUBJID, AESER, AESDTH, AESLIFE, AESHOSP, AESDISAB, AESCONG, AESMIE, AEACN_DECODE, AESEV ) %>% group_by(USUBJID) %>% mutate(maxsev = max(as.character(AESEV), na.rm = TRUE)) %>% ungroup() %>% mutate(maxsev = ifelse(is.na(maxsev), "Missing", maxsev)) %>% mutate( maxsev = factor(maxsev, levels = c("Mild", "Moderate", "Severe", "Missing")) ) adae <- inner_join(adae, adsl, by = c("USUBJID")) # Define layout and build table: 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 ) # Check the levels of AEACN_DECODE aeacn_levels <- levels(adae$AEACN_DECODE) # Here we are not considering "Drug Withdrawn", "Dose Not Changed", "Not Applicable" excl_aeacn_levels <- c("Drug Withdrawn", "Dose Not Changed", "Not Applicable") dosemod_lvls <- aeacn_levels[!(aeacn_levels %in% excl_aeacn_levels)] ## rearrange levels for AEACN_DECODE newsort_AEACN_DECODE <- unique(c( "Drug Interrupted", "Dose Reduced", "Dose Rate Reduced", "Dose Increased", "Unknown", aeacn_levels )) adae$AEACN_DECODE <- forcats::fct_relevel( adae$AEACN_DECODE, newsort_AEACN_DECODE ) ## mapping table for label updates dosemod_lblmap <- tibble(value = dosemod_lvls, label = dosemod_lvls) %>% mutate( label = case_when( value == "Dose Increased" ~ label, value == "Dose Reduced" ~ "Reduction of study treatment", value == "Drug Interrupted" ~ "Interruption of study treatment", TRUE ~ label ) ) dosemod_spf <- make_combo_splitfun( nm = "modified", label = "AE leading to dose modification of study", levels = c( "Dose Reduced", "Dose Increased", "Drug Interrupted", "Dose Rate Reduced", "Unknown" ) ) aesevall_spf <- make_combo_splitfun( nm = "AESEV_ALL", label = "Any AE~[super a]", levels = NULL ) rr_method <- "wald" ref_path <- c("colspan_trt", " ", trtvar, "Placebo") extra_args_rr <- list( method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) lyt <- basic_table( show_colcounts = TRUE, colcount_format = "N=xx", top_level_section_div = " " ) %>% append_topleft(c(" ", " ", "Event, n (%)")) %>% 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("Placebo") ) %>% split_rows_by( "AESER", split_fun = keep_split_levels("Y"), section_div = " " ) %>% summarize_row_groups( "AESER", cfun = a_freq_j, extra_args = list( label = "SAE", method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) ) %>% analyze( "AESDTH", afun = a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list(label = "With fatal outcome", val = "Y", NULL) ) ) %>% analyze( "AESLIFE", afun = a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list(label = "Life-threatening", val = "Y", NULL) ) ) %>% analyze( "AESHOSP", afun = a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list(label = "Requiring or prolonging hospitalization", val = "Y", NULL) ) ) %>% analyze( "AESDISAB", afun = a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list( label = "Resulting in persistent or significant disability/incapacity", val = "Y", NULL ) ) ) %>% analyze( "AESCONG", afun = a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list(label = "Congenital anomaly or birth defect", val = "Y", NULL) ) ) %>% analyze( "AESMIE", afun = a_freq_j, show_labels = "hidden", extra_args = append(extra_args_rr, list(label = "Other", val = "Y", NULL)) ) %>% analyze( "AEACN_DECODE", afun = a_freq_j, nested = FALSE, extra_args = append( extra_args_rr, list( label = "AE leading to permanent discontinuation of study treatment", val = "Drug Withdrawn", NULL ) ) ) %>% split_rows_by("AEACN_DECODE", split_fun = dosemod_spf, section_div = " ") %>% summarize_row_groups( "AEACN_DECODE", cfun = a_freq_j, extra_args = list( label = "AE leading to dose modification of study treatment", method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) ) %>% analyze( "AEACN_DECODE", table_names = "AEACN_DECODE", a_freq_j, show_labels = "hidden", extra_args = append( extra_args_rr, list( excl_levels = excl_aeacn_levels, label_map = dosemod_lblmap, drop_levels = TRUE ) ) ) %>% split_rows_by("maxsev", split_fun = aesevall_spf) %>% summarize_row_groups( "maxsev", cfun = a_freq_j, extra_args = list( label = "Any AE~[super a]", method = rr_method, ref_path = ref_path, .stats = c("count_unique_fraction") ) ) %>% analyze("maxsev", afun = a_freq_j, extra_args = append(extra_args_rr, NULL)) result <- build_table(lyt, adae, alt_counts_df = adsl) # Post-Processing: result <- remove_col_count(result) result <- safe_prune_table( result, prune_func = count_pruner( cat_exclude = c( "With fatal outcome", "Life-threatening", "Requiring or prolonging hospitalization", "Resulting in persistent or significant disability/incapacity", "Congenital anomaly or birth defect", "Other" ) ) ) # 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')`) ::::