gap (version 1.6)

ab: Test/Power calculation for mediating effect

Description

Test/Power calculation for mediating effect

Usage

ab(
  type = "power",
  n = 25000,
  a = 0.15,
  sa = 0.01,
  b = log(1.19),
  sb = 0.01,
  alpha = 0.05,
  fold = 1
)

Value

The returned value are z-test and significance level for significant testing or sample size/power for a given fold change of the default sample size.

Arguments

type

string option: "test", "power".

n

default sample size to be used for power calculation.

a

regression coefficient from indepdendent variable to mediator.

sa

SE(a).

b

regression coefficient from mediator variable to outcome.

sb

SE(b).

alpha

size of siginficance test for power calculation.

fold

fold change for power calculation, as appropriate for a range of sample sizes.

Author

Jing Hua Zhao

Details

This function tests for or obtains power of mediating effect based on estimates of two regression coefficients and their standard errors. Note that for binary outcome or mediator, one should use log-odds ratio and its standard error.

References

freathy08gap

Kline RB. Principles and practice of structural equation modeling, Second Edition. The Guilford Press 2005.

MacKinnon DP. Introduction to Statistical Mediation Analysis. Taylor & Francis Group 2008.

Preacher KJ, Leonardelli GJ. Calculation for the Sobel Test-An interactive calculation tool for mediation tests https://quantpsy.org/sobel/sobel.htm

See Also

ccsize

Examples

if (FALSE) {
ab()
n <- power <- vector()
for (j in 1:10)
{
   z <- ab(fold=j*0.01)
   n[j] <- z[1]
   power[j] <- z[2]
}
plot(n,power,xlab="Sample size",ylab="Power")
title("SNP-BMI-T2D association in EPIC-Norfolk study")
}