getDesignRiskDiffExactEquiv {lrstat}

Power and sample size for exact unconditional test for equivalence in risk difference

Description

Obtains the power given sample size or obtains the sample size given power for exact unconditional test of equivalence in risk difference.

Usage

getDesignRiskDiffExactEquiv(
  beta = NA_real_,
  n = NA_real_,
  riskDiffLower = NA_real_,
  riskDiffUpper = NA_real_,
  pi1 = NA_real_,
  pi2 = NA_real_,
  allocationRatioPlanned = 1,
  alpha = 0.05
)

Arguments

beta

The type II error.

n

The total sample size.

riskDiffLower

The lower equivalence limit of risk difference.

riskDiffUpper

The upper equivalence limit of risk difference.

pi1

The assumed probability for the active treatment group.

pi2

The assumed probability for the control group.

allocationRatioPlanned

Allocation ratio for the active treatment versus control. Defaults to 1 for equal randomization.

alpha

The significance level for each of the two one-sided tests. Defaults to 0.05.

Value

A data frame with the following variables:

• alpha: The specified significance level for each of the two one-sided tests.

• attainedAlpha: The attained significance level.

• power: The power.

• n: The sample size.

• riskDiffLower: The lower equivalence limit of risk difference.

• riskDiffUpper: The upper equivalence limit of risk difference.

• pi1: The assumed probability for the active treatment group.

• pi2: The assumed probability for the control group.

• riskDiff: The risk difference.

• allocationRatioPlanned: Allocation ratio for the active treatment versus control.

• zstatRiskDiffLower: The efficacy boundaries on the z-test statistic scale for the one-sided null hypothesis on the lower equivalence limit.

• zstatRiskDiffUpper: The efficacy boundaries on the z-test statistic scale for the one-sided null hypothesis on the upper equivalence limit.

Author(s)

Kaifeng Lu, [e-mail]

Examples

getDesignRiskDiffExactEquiv(
  n = 200, riskDiffLower = -0.2, riskDiffUpper = 0.2,
  pi1 = 0.775, pi2 = 0.775, alpha = 0.05)