BinaryPowerBSSR {bbssr}R Documentation

Power Calculation for Two-Arm Trials with Binary Endpoints Using Blinded Sample Size Re-estimation (BSSR)

Description

Calculates the power for two-arm trials with binary endpoints when blinded sample size re-estimation (BSSR) is implemented. The function supports five different statistical tests and allows for both restricted and unrestricted designs with optional weighted approaches.

Usage

BinaryPowerBSSR(
  asmd.p1,
  asmd.p2,
  p,
  Delta.A,
  Delta.T,
  N1,
  N2,
  omega,
  r,
  alpha,
  tar.power,
  Test,
  restricted,
  weighted
)

Arguments

asmd.p1

Assumed proportion of responders for group 1

asmd.p2

Assumed proportion of responders for group 2

p

Vector of pooled proportions of responders from both groups (can specify multiple values)

Delta.A

Assumed treatment effect (risk difference)

Delta.T

True treatment effect (risk difference)

N1

Initial sample size of group 1

N2

Initial sample size of group 2

omega

Fraction of sample size used for interim analysis (i.e., for BSSR)

r

Allocation ratio to group 1

alpha

One-sided level of significance

tar.power

Target power

Test

Type of statistical test. Options: 'Chisq', 'Fisher', 'Fisher-midP', 'Z-pool', or 'Boschloo'

restricted

Logical. If TRUE, restricted design is chosen

weighted

Logical. If TRUE, weighted approach is chosen

Details

The function supports the following five one-sided tests:

Value

A data frame containing:

p1

True probability of responders for group 1

p2

True probability of responders for group 2

p

True probability of pooled responders from both groups

power.BSSR

Power for BSSR design

power.TRAD

Power for traditional design

Author(s)

Gosuke Homma (my.name.is.gosuke@gmail.com)

Examples

# Simple BSSR calculation with fast Chi-squared test
result1 <- BinaryPowerBSSR(
  asmd.p1 = 0.6, asmd.p2 = 0.3,
  p = 0.45,
  Delta.A = 0.3, Delta.T = 0.3,
  N1 = 5, N2 = 5, omega = 0.5, r = 1,
  alpha = 0.025, tar.power = 0.8,
  Test = 'Chisq',
  restricted = FALSE, weighted = FALSE
)
print(result1)


# More computationally intensive BSSR examples
result2 <- BinaryPowerBSSR(
  asmd.p1 = 0.45,
  asmd.p2 = 0.09,
  p = seq(0.14, 0.23, by = 0.01),
  Delta.A = 0.36,
  Delta.T = 0.36,
  N1 = 24,
  N2 = 24,
  omega = 0.5,
  r = 1,
  alpha = 0.025,
  tar.power = 0.8,
  Test = 'Z-pool',
  restricted = FALSE,
  weighted = TRUE
)
print(result2)



[Package bbssr version 1.0.2 Index]