## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

## ----setup, message=FALSE, warning=FALSE--------------------------------------
library(twoCoprimary)
library(dplyr)
library(tidyr)
library(knitr)

## ----table4_homma-------------------------------------------------------------
# Recreate Homma and Yoshida (2025) Table 4
library(dplyr)
library(tidyr)
library(readr)

param_grid_bin_exact_ss <- tibble(
  p11 = 0.54, 
  p12 = 0.54,
  p21 = 0.25,
  p22 = 0.25
)

result_bin_exact_ss <- do.call(
  bind_rows,
  lapply(c("Chisq", "Fisher", "Z-pool", "Boschloo"), function(test) {
    do.call(
      bind_rows,
      lapply(1:2, function(r) {
        design_table(
          param_grid = param_grid_bin_exact_ss,
          rho_values = c(0, 0.3, 0.5, 0.8),
          r = r,
          alpha = 0.025,
          beta = 0.1,
          endpoint_type = "binary",
          Test = test
        ) %>% 
          mutate(alpha = 0.025, r = r, Test = test)
      })
    )
  })
) %>% 
  pivot_longer(
    cols = starts_with("rho_"),
    names_to = "rho",
    values_to = "N",
    names_transform = list(rho = parse_number)
  ) %>% 
  select(r, rho, Test, N) %>% 
  pivot_wider(names_from = Test,  values_from = N) %>% 
  as.data.frame()

kable(result_bin_exact_ss,
      caption = "Table 4: Total Sample Size (N) for Two Co-Primary Binary Endpoints (α = 0.025, 1-β = 0.90)^a,b^",
      digits = 1,
      col.names = c("r", "ρ", "Chisq", "Fisher", "Z-pool", "Boschloo"))

## ----example1-----------------------------------------------------------------
# Calculate exact power using Fisher's exact test
result_fisher <- power2BinaryExact(
  n1 = 50,
  n2 = 50,
  p11 = 0.70, p12 = 0.65,
  p21 = 0.50, p22 = 0.45,
  rho1 = 0.5, rho2 = 0.5,
  alpha = 0.025,
  Test = "Fisher"
)

print(result_fisher)

## ----example2-----------------------------------------------------------------
# Calculate required sample size using Boschloo's test
result_ss <- ss2BinaryExact(
  p11 = 0.70, p12 = 0.65,
  p21 = 0.50, p22 = 0.45,
  rho1 = 0.5, rho2 = 0.5,
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  Test = "Boschloo"
)

print(result_ss)

## ----example3-----------------------------------------------------------------
# Compare different exact test methods
test_methods <- c("Chisq", "Fisher", "Fisher-midP", "Z-pool", "Boschloo")

comparison <- lapply(test_methods, function(test) {
    result <- ss2BinaryExact(
        p11 = 0.50, p12 = 0.40,
        p21 = 0.20, p22 = 0.10,
        rho1 = 0.7, rho2 = 0.6,
        r = 1,
        alpha = 0.025,
        beta = 0.2,
        Test = test
    )
    data.frame(
        Test = test,
        n2 = result$n2,
        N = result$N
    )
})

comparison_table <- bind_rows(comparison)

kable(comparison_table,
      caption = "Sample Size Comparison Across Test Methods",
      col.names = c("Test Method", "n per group", "N total"))

## ----example4-----------------------------------------------------------------
# Calculate sample size for different correlation values
rho_values <- c(0, 0.3, 0.5, 0.8)

correlation_effect <- lapply(rho_values, function(rho) {
    result <- ss2BinaryExact(
        p11 = 0.70, p12 = 0.60,
        p21 = 0.40, p22 = 0.30,
        rho1 = rho, rho2 = rho,
        r = 1,
        alpha = 0.025,
        beta = 0.2,
        Test = "Fisher"
    )
    data.frame(
        rho = rho,
        n2 = result$n2,
        N = result$N
    )
})

rho_table <- bind_rows(correlation_effect)

kable(rho_table,
      caption = "Impact of Correlation on Sample Size (Fisher's Test)",
      col.names = c("ρ", "n per group", "N total"))

## ----example5-----------------------------------------------------------------
# Exact method (Chisq)
exact_result <- ss2BinaryExact(
    p11 = 0.60, p12 = 0.40,
    p21 = 0.30, p22 = 0.10,
    rho1 = 0.5, rho2 = 0.5,
    r = 1,
    alpha = 0.025,
    beta = 0.1,
    Test = "Chisq"
)

# Asymptotic method (AN)
asymp_result <- ss2BinaryApprox(
    p11 = 0.60, p12 = 0.40,
    p21 = 0.30, p22 = 0.10,
    rho1 = 0.5, rho2 = 0.5,
    r = 1,
    alpha = 0.025,
    beta = 0.1,
    Test = "AN"
)

comparison_exact_asymp <- data.frame(
  Method = c("Exact (Chisq)", "Asymptotic (AN)"),
  n_per_group = c(exact_result$n2, asymp_result$n2),
  N_total = c(exact_result$N, asymp_result$N),
  Difference = c(0, asymp_result$N - exact_result$N)
)

kable(comparison_exact_asymp,
      caption = "Comparison: Exact vs Asymptotic Methods",
      col.names = c("Method", "n per group", "N total", "Difference"))