Electrocardiogram Summary

QTc Categories and Change from Baseline

Setup

See Prerequisites for installation instructions.

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

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

# QTcF post-baseline on-treatment records
adeg_qtcf <- pharmaverseadam::adeg |>
  blank_to_na() |>
  filter(
    SAFFL   == "Y",
    TRT01A  != "Screen Failure",
    PARAMCD == "QTCFR",
    ONTRTFL == "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)

# Category ordered levels (factor ensures 0-count levels appear)
avalcat_levels <- c("<= 450 ms", ">450<=480 ms", ">480<=500 ms", ">500 ms")

adeg_qtcf <- adeg_qtcf |>
  mutate(AVALCAT1 = factor(AVALCAT1, levels = avalcat_levels))
chgcat_levels  <- c("<= 30 ms", ">30<=60 ms", ">60 ms")

Data Preparation

dplyr

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

# ── Section 1: Worst post-baseline AVALCAT1 per subject ──
# "Worst" = highest category (>500 ms > >480<=500 ms > >450<=480 ms > <= 450 ms)
cat1_order <- c("<= 450 ms" = 1L, ">450<=480 ms" = 2L,
                ">480<=500 ms" = 3L, ">500 ms" = 4L)

worst_cat <- adeg_qtcf |>
  filter(!is.na(AVALCAT1)) |>
  mutate(cat_rank = cat1_order[AVALCAT1]) |>
  group_by(USUBJID, TRT01A) |>
  slice_max(cat_rank, n = 1L, with_ties = FALSE) |>
  ungroup()

sec1_arm <- worst_cat |>
  count(AVALCAT1, TRT01A) |>
  complete(AVALCAT1 = avalcat_levels, TRT01A = arm_levels, fill = list(n = 0L)) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(AVALCAT1, TRT01A, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

sec1_total <- worst_cat |>
  count(AVALCAT1) |>
  complete(AVALCAT1 = avalcat_levels, fill = list(n = 0L)) |>
  mutate(Total = n_pct(n, N_total)) |>
  select(AVALCAT1, Total)

sec1_wide <- left_join(sec1_arm, sec1_total, by = "AVALCAT1") |>
  mutate(
    section  = "Worst Post-Baseline QTcF Category",
    category = AVALCAT1,
    .before  = 1
  ) |>
  select(-AVALCAT1) |>
  mutate(across(all_of(c(arm_levels, "Total")), ~ replace_na(.x, "0 (0.0)")))

# ── Section 2: Maximum change from baseline categories per subject ──
max_chg <- adeg_qtcf |>
  filter(!is.na(CHG)) |>
  group_by(USUBJID, TRT01A) |>
  summarise(max_chg = max(CHG, na.rm = TRUE), .groups = "drop")

chg_row <- function(threshold, label) {
  max_chg |>
    filter(max_chg > threshold) |>
    count(TRT01A) |>
    complete(TRT01A = arm_levels, fill = list(n = 0L)) |>
    mutate(
      total_n  = sum(max_chg$max_chg > threshold),
      value    = mapply(n_pct, n, arm_n[TRT01A]),
      Total    = n_pct(total_n, N_total),
      section  = "Maximum Increase from Baseline",
      category = label,
      .before  = 1
    ) |>
    select(section, category, TRT01A, value, Total) |>
    pivot_wider(names_from = TRT01A, values_from = value)
}

sec2_wide <- bind_rows(
  chg_row(30, "> 30 ms"),
  chg_row(60, "> 60 ms")
)

# ── Combine sections ──
ecg_wide <- bind_rows(sec1_wide, sec2_wide)

cards

# ── Section 1: worst post-baseline AVALCAT1 per subject ──
cat1_order <- c("<= 450 ms" = 1L, ">450<=480 ms" = 2L,
                ">480<=500 ms" = 3L, ">500 ms" = 4L)

worst_cat_c <- adeg_qtcf |>
  filter(!is.na(AVALCAT1)) |>
  mutate(cat_rank = cat1_order[AVALCAT1]) |>
  group_by(USUBJID, TRT01A) |>
  slice_max(cat_rank, n = 1L, with_ties = FALSE) |>
  ungroup() |>
  select(USUBJID, TRT01A, AVALCAT1)

sec1_ard <- ard_stack(
  data = worst_cat_c,
  .by  = "TRT01A",
  ard_categorical(variables = AVALCAT1),
  .overall = TRUE
)

sec1_cards <- fr_wide_ard(
  sec1_ard,
  statistic = list(categorical = "{n} ({p}%)"),
  decimals  = c(p = 1)
) |>
  filter(variable == "AVALCAT1") |>
  mutate(
    section  = "Worst Post-Baseline QTcF Category",
    category = stat_label
  ) |>
  select(section, category, all_of(c(arm_levels, "Total")))

# ── Section 2: maximum change from baseline categories ──
max_chg_c <- adeg_qtcf |>
  filter(!is.na(CHG)) |>
  group_by(USUBJID, TRT01A) |>
  summarise(max_chg = max(CHG, na.rm = TRUE), .groups = "drop") |>
  mutate(
    chg_gt30 = max_chg > 30,
    chg_gt60 = max_chg > 60
  )

sec2_ard <- ard_stack(
  data = max_chg_c,
  .by  = "TRT01A",
  ard_dichotomous(
    variables = c(chg_gt30, chg_gt60),
    value     = list(chg_gt30 = TRUE, chg_gt60 = TRUE)
  ),
  .overall = TRUE
)

sec2_cards <- fr_wide_ard(
  sec2_ard,
  statistic = list(dichotomous = "{n} ({p}%)"),
  decimals  = c(p = 1),
  label     = c(chg_gt30 = "> 30 ms", chg_gt60 = "> 60 ms")
) |>
  mutate(
    section  = "Maximum Increase from Baseline",
    category = stat_label
  ) |>
  select(section, category, all_of(c(arm_levels, "Total")))

ecg_cards <- bind_rows(sec1_cards, sec2_cards)

arframe Pipeline

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

ecg_wide |>
  fr_table() |>
  fr_titles(
    "Table 14.3.7",
    "Electrocardiogram Summary",
    "QTcF Categories and Change from Baseline \u2014 Safety Population"
  ) |>
  fr_cols(
    section  = fr_col(visible = FALSE),
    category = fr_col("Category", width = 2.8),
    !!!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    = list(cols = "section", label = "category"),
    blank_after = "section",
    group_style = list(bold = TRUE)
  ) |>
  fr_footnotes(
    "QTcF = QT interval corrected using Fridericia formula (PARAMCD = QTCFR).",
    "Section 1: worst post-baseline category based on highest observed AVALCAT1 rank per subject.",
    "Section 2: maximum individual change from baseline (CHG) across all post-baseline visits.",
    "On-treatment records only (ONTRTFL = Y).",
    "Percentages based on N per treatment arm (Safety Population)."
  )

Rendered Table

Table 14.3.7

Electrocardiogram Summary

QTcF Categories and Change from Baseline — Safety Population

Category	Placebo (N=86)	Xanomeline High Dose (N=72)	Xanomeline Low Dose (N=96)	Total (N=254)
Worst Post-Baseline QTcF Category
<= 450 ms	0	0	0	0
>450<=480 ms	0	0	0	0
>480<=500 ms	0	0	0	0
>500 ms	84 (97.7)	72 (100.0)	94 (97.9)	250 (98.4)
Maximum Increase from Baseline
> 30 ms	82 (95.3)	68 ( 94.4)	86 (89.6)	236 (92.9)
> 60 ms	78 (90.7)	63 ( 87.5)	76 (79.2)	217 (85.4)

QTcF = QT interval corrected using Fridericia formula (PARAMCD = QTCFR).

Section 1: worst post-baseline category based on highest observed AVALCAT1 rank per subject.

Section 2: maximum individual change from baseline (CHG) across all post-baseline visits.

On-treatment records only (ONTRTFL = Y).

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

/opt/quarto/share/rmd/rmd.R 01APR2026 09:52:56