Serious Adverse Events by SOC and PT

SAEs by System Organ Class and Preferred Term

Setup

See Prerequisites for installation instructions.

library(arframe)
library(pharmaverseadam)
library(dplyr, warn.conflicts = FALSE)
library(tidyr)
library(cards)

adsl_saf <- pharmaverseadam::adsl |>
  blank_to_na() |>
  filter(SAFFL == "Y", TRT01A != "Screen Failure")

adae_sae <- pharmaverseadam::adae |>
  blank_to_na() |>
  filter(SAFFL == "Y", TRTEMFL == "Y", AESER == "Y")

arm_levels <- c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose")
arm_n <- adsl_saf |>
  filter(TRT01A %in% arm_levels) |>
  count(TRT01A) |>
  pull(n, name = TRT01A)
arm_n <- arm_n[arm_levels]
N_total <- nrow(adsl_saf)
n_vec <- c(arm_n, Total = N_total)

Data Preparation

dplyr

n_pct <- function(n, denom) sprintf("%d (%.1f)", n, n / denom * 100)

# ── Any SAE row ──
any_sae_arm <- adae_sae |>
  distinct(USUBJID, TRT01A) |>
  count(TRT01A) |>
  filter(TRT01A %in% arm_levels) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(TRT01A, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

any_sae_total <- n_distinct(adae_sae$USUBJID)

any_sae_row <- bind_cols(
  tibble(soc = "ANY SAE", pt = "ANY SAE", row_type = "overall"),
  any_sae_arm,
  tibble(Total = n_pct(any_sae_total, N_total))
)

# ── SOC-level ──
soc_arm <- adae_sae |>
  distinct(USUBJID, TRT01A, AEBODSYS) |>
  count(TRT01A, AEBODSYS) |>
  filter(TRT01A %in% arm_levels) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(TRT01A, AEBODSYS, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

soc_total <- adae_sae |>
  distinct(USUBJID, AEBODSYS) |>
  count(AEBODSYS) |>
  mutate(Total = n_pct(n, N_total)) |>
  select(AEBODSYS, Total)

soc_wide <- left_join(soc_arm, soc_total, by = "AEBODSYS") |>
  mutate(soc = AEBODSYS, pt = AEBODSYS, row_type = "soc", .before = 1) |>
  select(-AEBODSYS)

# ── PT-level ──
pt_arm <- adae_sae |>
  distinct(USUBJID, TRT01A, AEBODSYS, AEDECOD) |>
  count(TRT01A, AEBODSYS, AEDECOD) |>
  filter(TRT01A %in% arm_levels) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(TRT01A, AEBODSYS, AEDECOD, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

pt_total <- adae_sae |>
  distinct(USUBJID, AEBODSYS, AEDECOD) |>
  count(AEBODSYS, AEDECOD) |>
  mutate(Total = n_pct(n, N_total)) |>
  select(AEBODSYS, AEDECOD, Total)

pt_wide <- left_join(pt_arm, pt_total, by = c("AEBODSYS", "AEDECOD")) |>
  mutate(soc = AEBODSYS, pt = AEDECOD, row_type = "pt", .before = 1) |>
  select(-AEBODSYS, -AEDECOD)

# ── Sort by frequency and interleave ──
soc_order <- adae_sae |>
  distinct(USUBJID, AEBODSYS) |>
  count(AEBODSYS, name = "soc_n") |>
  arrange(desc(soc_n)) |>
  pull(AEBODSYS)

sae_wide <- bind_rows(
  any_sae_row,
  bind_rows(lapply(soc_order, function(s) {
    bind_rows(
      filter(soc_wide, soc == s),
      filter(pt_wide, soc == s) |>
        left_join(
          adae_sae |> distinct(USUBJID, AEBODSYS, AEDECOD) |>
            count(AEBODSYS, AEDECOD, name = "pt_n"),
          by = c("soc" = "AEBODSYS", "pt" = "AEDECOD")
        ) |>
        arrange(desc(pt_n)) |>
        select(-pt_n)
    )
  }))
) |>
  mutate(across(where(is.character) & !c(soc, pt, row_type),
                ~ replace_na(.x, "0 (0.0)")))

# Ensure all arm columns exist (some arms may have zero SAEs)
for (a in arm_levels) {
  if (!a %in% names(sae_wide)) sae_wide[[a]] <- "0 (0.0)"
}

cards

sae_ard <- ard_stack_hierarchical(
  data        = adae_sae,
  variables   = c(AEBODSYS, AEDECOD),
  by          = TRT01A,
  denominator = adsl_saf,
  id          = USUBJID,
  overall     = TRUE,
  over_variables = TRUE
) |>
  sort_ard_hierarchical(sort = "descending")

sae_wide_cards <- fr_wide_ard(
  sae_ard,
  statistic = "{n} ({p}%)",
  decimals  = c(p = 1),
  label     = c(
    "..ard_hierarchical_overall.." = "ANY SAE",
    AEBODSYS = "System Organ Class",
    AEDECOD  = "Preferred Term"
  )
)

arframe Pipeline

The rendered table below uses the dplyr data prep (sae_wide). The cards tab produces an equivalent sae_wide_cards — swap it in to use the cards path instead.

sae_wide |>
  fr_table() |>
  fr_titles(
    "Table 14.3.2",
    "Patients With Serious Adverse Events by System Organ Class and Preferred Term",
    "Safety Population"
  ) |>
  fr_cols(
    soc      = fr_col(visible = FALSE),
    pt       = fr_col("System Organ Class\n  Preferred Term", width = 3.5),
    row_type = fr_col(visible = FALSE),
    !!!setNames(
      lapply(arm_levels, function(a) fr_col(a, align = "decimal")),
      arm_levels
    ),
    Total = fr_col("Total", align = "decimal"),
    .n = n_vec
  ) |>
  fr_header(bold = TRUE, align = "center") |>
  fr_rows(
    group_by  = "soc",
    indent_by = "pt"
  ) |>
  fr_styles(
    fr_row_style(rows = fr_rows_matches("row_type", value = "soc"), bold = TRUE),
    fr_row_style(rows = fr_rows_matches("row_type", value = "overall"), bold = TRUE)
  ) |>
  fr_footnotes(
    "SAE = Serious Adverse Event (any AE that results in death, is life-threatening, requires hospitalization, etc.).",
    "Subjects counted once per SOC and once per PT.",
    "Percentages based on N per treatment arm (Safety Population).",
    "SOC sorted by descending frequency; PT sorted within SOC by descending frequency."
  )

Rendered Table

Table 14.3.2

Patients With Serious Adverse Events by System Organ Class and Preferred Term

Safety Population

System Organ Class Preferred Term	Xanomeline High Dose (N=72)	Xanomeline Low Dose (N=96)	Total (N=254)	Placebo (N=86)
ANY SAE	1 (1.4)	2 (2.1)	3 (1.2)	0
NERVOUS SYSTEM DISORDERS	1 (1.4)	2 (2.1)	3 (1.2)	0
SYNCOPE	0	2 (2.1)	2 (0.8)	0
PARTIAL SEIZURES WITH SECONDARY GENERALISATION	1 (1.4)	0	1 (0.4)	0

SAE = Serious Adverse Event (any AE that results in death, is life-threatening, requires hospitalization, etc.).

Subjects counted once per SOC and once per PT.

Percentages based on N per treatment arm (Safety Population).

SOC sorted by descending frequency; PT sorted within SOC by descending frequency.

/opt/quarto/share/rmd/rmd.R 01APR2026 09:52:21