Enrollment by Country and Site

Summary of Enrollment by Country and Investigative Site

Setup

See Prerequisites for installation instructions.

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

# All enrolled subjects (no safety filter — enrollment includes screen failures)
adsl_all <- pharmaverseadam::adsl |>
  blank_to_na() |>
  filter(TRT01A != "Screen Failure")

arm_levels <- c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose")

arm_n <- adsl_all |>
  filter(TRT01A %in% arm_levels) |>
  count(TRT01A) |>
  pull(n, name = TRT01A)
arm_n <- arm_n[arm_levels]

N_total <- sum(arm_n)
n_vec <- c(arm_n, Total = N_total)

Data Preparation

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

# Assign country (CDISCPILOT01 is all USA — use COUNTRY column)
# Group sites under their country for the hierarchical display
adsl_enroll <- adsl_all |>
  filter(TRT01A %in% arm_levels) |>
  mutate(
    COUNTRY = if_else(is.na(COUNTRY) | COUNTRY == "", "USA", COUNTRY),
    site_label = paste0("Site ", SITEID)
  )

# ── Country-level counts ──
country_arm <- adsl_enroll |>
  count(TRT01A, COUNTRY) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(TRT01A, COUNTRY, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

country_total <- adsl_enroll |>
  count(COUNTRY) |>
  mutate(Total = n_pct(n, N_total)) |>
  select(COUNTRY, Total)

country_wide <- left_join(country_arm, country_total, by = "COUNTRY") |>
  mutate(
    country  = COUNTRY,
    site     = COUNTRY,
    row_type = "country",
    .before  = 1
  ) |>
  select(-COUNTRY)

# ── Site-level counts ──
site_arm <- adsl_enroll |>
  count(TRT01A, COUNTRY, site_label) |>
  mutate(value = mapply(n_pct, n, arm_n[TRT01A])) |>
  select(TRT01A, COUNTRY, site_label, value) |>
  pivot_wider(names_from = TRT01A, values_from = value)

site_total <- adsl_enroll |>
  count(COUNTRY, site_label) |>
  mutate(Total = n_pct(n, N_total)) |>
  select(COUNTRY, site_label, Total)

site_wide <- left_join(site_arm, site_total, by = c("COUNTRY", "site_label")) |>
  mutate(
    country  = COUNTRY,
    site     = site_label,
    row_type = "site",
    .before  = 1
  ) |>
  select(-COUNTRY, -site_label)

# ── Sort: country alphabetical, sites by descending enrollment ──
site_order <- adsl_enroll |>
  count(COUNTRY, site_label, name = "n") |>
  arrange(COUNTRY, desc(n)) |>
  select(country = COUNTRY, site = site_label)

site_wide <- site_order |>
  left_join(site_wide, by = c("country", "site"))

# ── Interleave country + site rows ──
enroll_wide <- bind_rows(
  lapply(sort(unique(adsl_enroll$COUNTRY)), function(cntry) {
    bind_rows(
      filter(country_wide, country == cntry),
      filter(site_wide, country == cntry)
    )
  })
) |>
  mutate(across(all_of(c(arm_levels, "Total")), ~ replace_na(.x, "0 (0.0)")))

arframe Pipeline

The fr_table() pipeline:

enroll_wide |>
  fr_table() |>
  fr_titles(
    "Table 14.1.2",
    "Summary of Enrollment by Country and Investigative Site",
    "Randomized Subjects"
  ) |>
  fr_cols(
    country  = fr_col(visible = FALSE),
    site     = fr_col("Country\n  Site", width = 2.5),
    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  = "country",
    indent_by = "site"
  ) |>
  fr_styles(
    fr_row_style(rows = fr_rows_matches("row_type", value = "country"), bold = TRUE)
  ) |>
  fr_footnotes(
    "Percentages based on total number of randomized subjects.",
    "Subjects who were screen failures are excluded.",
    "Sites sorted by descending enrollment count within each country."
  )

Rendered Table

Table 14.1.2

Summary of Enrollment by Country and Investigative Site

Randomized Subjects

Country Site	Placebo (N=86)	Xanomeline High Dose (N=72)	Xanomeline Low Dose (N=96)	Total (N=254)
USA	86 (100.0)	72 (100.0)	96 (100.0)	254 (100.0)
Site 701	14 ( 16.3)	12 ( 16.7)	15 ( 15.6)	41 ( 16.1)
Site 710	11 ( 12.8)	10 ( 13.9)	10 ( 10.4)	31 ( 12.2)
Site 704	9 ( 10.5)	8 ( 11.1)	8 ( 8.3)	25 ( 9.8)
Site 708	9 ( 10.5)	5 ( 6.9)	11 ( 11.5)	25 ( 9.8)
Site 716	8 ( 9.3)	7 ( 9.7)	9 ( 9.4)	24 ( 9.4)
Site 709	7 ( 8.1)	5 ( 6.9)	9 ( 9.4)	21 ( 8.3)
Site 703	6 ( 7.0)	5 ( 6.9)	7 ( 7.3)	18 ( 7.1)
Site 705	5 ( 5.8)	5 ( 6.9)	6 ( 6.2)	16 ( 6.3)
Site 718	4 ( 4.7)	4 ( 5.6)	5 ( 5.2)	13 ( 5.1)
Site 713	3 ( 3.5)	3 ( 4.2)	3 ( 3.1)	9 ( 3.5)
Site 715	3 ( 3.5)	2 ( 2.8)	3 ( 3.1)	8 ( 3.1)
Site 717	2 ( 2.3)	3 ( 4.2)	2 ( 2.1)	7 ( 2.8)
Site 714	2 ( 2.3)	1 ( 1.4)	3 ( 3.1)	6 ( 2.4)
Site 711	1 ( 1.2)	1 ( 1.4)	2 ( 2.1)	4 ( 1.6)
Site 706	1 ( 1.2)	1 ( 1.4)	1 ( 1.0)	3 ( 1.2)
Site 707	1 ( 1.2)	0	1 ( 1.0)	2 ( 0.8)
Site 702	0	0	1 ( 1.0)	1 ( 0.4)

Percentages based on total number of randomized subjects.

Subjects who were screen failures are excluded.

Sites sorted by descending enrollment count within each country.

/opt/quarto/share/rmd/rmd.R 01APR2026 09:53:04