twoCoprimary (version 1.0.0)

design_table: Create Design Comparison Table for Two Co-Primary Endpoints

Description

Generates a comprehensive table comparing sample sizes or power across different parameter combinations and correlation values. This function is useful for sensitivity analyses and exploring how design parameters affect statistical properties.

Usage

design_table(
  param_grid,
  rho_values = c(0, 0.3, 0.5, 0.8),
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  endpoint_type = c("continuous", "binary", "mixed_cont_binary", "mixed_count_cont"),
  Test = "AN",
  known_var = TRUE,
  nMC = 1000,
  output_var = NULL
)

Value

A data.frame of class "twoCoprimary_table" with:

• Parameter columns (from param_grid)

• Result columns for each correlation value (rho_0.0, rho_0.3, etc.)

Arguments

param_grid

A data.frame containing parameter combinations. Required columns depend on endpoint_type and calculation_mode:

• For continuous endpoints (sample size): delta1, delta2, sd1, sd2

• For continuous endpoints (power): n1, n2, delta1, delta2, sd1, sd2

• For binary endpoints (sample size): p11, p12, p21, p22

• For binary endpoints (power): n1, n2, p11, p12, p21, p22

• For mixed continuous-binary (sample size): delta, sd, p1, p2

• For mixed continuous-binary (power): n1, n2, delta, sd, p1, p2

• For mixed count-continuous (sample size): r1, r2, nu, t, mu1, mu2, sd

• For mixed count-continuous (power): n1, n2, r1, r2, nu, t, mu1, mu2, sd

rho_values

Numeric vector of correlation values to evaluate. Default is c(0, 0.3, 0.5, 0.8).

r

Allocation ratio (n1/n2). Required for sample size calculation. Default is 1.

alpha

One-sided significance level. Default is 0.025.

beta

Type II error rate (1 - power). Required for sample size calculation. Default is 0.2 (power = 0.8).

endpoint_type

Character string specifying endpoint type: "continuous", "binary", "mixed_cont_binary", or "mixed_count_cont".

Test

Test method for binary endpoints: "AN" (asymptotic normal), "ANc" (with continuity correction), "AS" (arcsine), or "ASc". Default is "AN". Only used for binary and mixed_cont_binary endpoints.

known_var

Logical indicating whether variance is known for continuous endpoints. Default is TRUE.

nMC

Number of Monte Carlo simulations for certain calculations. Default is 1000.

output_var

Character string specifying which variable to output in the result columns: "N" (total sample size, default for sample size calculation) or "powerCoprimary" (co-primary power, default for power calculation).

Details

This function performs systematic calculations across all combinations of parameters specified in param_grid and correlation values in rho_values.

The calculation mode (sample size vs power) is automatically determined:

• If param_grid contains n1 and n2: calculates power

• Otherwise: calculates sample size (requires r, alpha, beta)

For binary endpoints with two correlations (rho1, rho2), both are set to the same value from rho_values for each calculation.

The output format follows the style of Sozu et al. (2011), with parameters displayed in the leftmost columns and results for each correlation in subsequent columns.

References

Sozu, T., Kanou, T., Hamada, C., & Yoshimura, I. (2011). Power and sample size calculations in clinical trials with multiple primary variables. Japanese Journal of Biometrics, 27, 83-96.

Examples

# Sample size calculation for continuous endpoints
param_grid <- expand.grid(
  delta1 = c(0.3, 0.5),
  delta2 = c(0.1, 0.2, 0.3),
  sd1 = c(1.0, 1.5),
  sd2 = c(1.0, 1.5)
)

result <- design_table(
  param_grid = param_grid,
  rho_values = c(0, 0.3, 0.5, 0.8),
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  endpoint_type = "continuous"
)
print(result)

# Power calculation for continuous endpoints
param_grid_power <- expand.grid(
  n1 = c(50, 100),
  n2 = c(50, 100),
  delta1 = 0.5,
  delta2 = 0.5,
  sd1 = 1.0,
  sd2 = 1.0
)

result_power <- design_table(
  param_grid = param_grid_power,
  rho_values = c(0, 0.3, 0.5, 0.8),
  alpha = 0.025,
  endpoint_type = "continuous"
)
print(result_power)

# Binary endpoints
param_grid_binary <- expand.grid(
  p11 = c(0.6, 0.7),
  p12 = c(0.4, 0.5),
  p21 = c(0.4, 0.5),
  p22 = c(0.2, 0.3)
)

result_binary <- design_table(
  param_grid = param_grid_binary,
  rho_values = c(0.3, 0.5, 0.7),
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  endpoint_type = "binary",
  Test = "AN"
)
print(result_binary)