ssMediation.VSMc.cox: Sample size for testing mediation effect in cox regression...

In powerMediation: Power/Sample Size Calculation for Mediation Analysis

Description

Calculate sample size for testing mediation effect in cox regression based on Vittinghoff, Sen and McCulloch's (2009) method.

Usage

ssMediation.VSMc.cox(power, 
                     b2, 
                     sigma.m, 
                     psi, 
                     corr.xm, 
                     n.lower = 1, 
                     n.upper = 1e+30, 
                     alpha = 0.05, 
                     verbose=TRUE)

Arguments

power	power for testing b_2=0 for the cox regression \log(λ)=\log(λ_0)+b1 x_i + b2 m_i, where λ is the hazard function and λ_0 is the baseline hazard function.
b2	regression coefficient for the mediator m in the cox regression \log(λ)=\log(λ_0)+b1 x_i + b2 m_i, where λ is the hazard function and λ_0 is the baseline hazard function.
sigma.m	standard deviation of the mediator.
psi	the probability that an observation is uncensored, so that the number of event d= n * psi, where n is the sample size.
corr.xm	correlation between the predictor x and the mediator m.
n.lower	lower bound for the sample size.
n.upper	upper bound for the sample size.
alpha	type I error rate.
verbose	logical. TRUE means printing sample size; FALSE means not printing sample size.

Details

The test is for testing the null hypothesis b_2=0 versus the alternative hypothesis b_2\neq 0 for the cox regressions:

\log(λ)=\log(λ_0)+b1 x_i + b2 m_i

Vittinghoff et al. (2009) showed that for the above cox regression, testing the mediation effect is equivalent to testing the null hypothesis H_0: b_2=0 versus the alternative hypothesis H_a: b_2\neq 0.

The full model is

\log(λ)=\log(λ_0)+b_1 x_i + b_2 m_i

The reduced model is

\log(λ)=\log(λ_0)+b_1 x_i

Vittinghoff et al. (2009) mentioned that if confounders need to be included in both the full and reduced models, the sample size/power calculation formula could be accommodated by redefining corr.xm as the multiple correlation of the mediator with the confounders as well as the predictor.

Value

n	sample size.
res.uniroot	results of optimization to find the optimal sample size.

Note

The test is a two-sided test. For one-sided tests, please double the significance level. For example, you can set alpha=0.10 to obtain one-sided test at 5% significance level.

Author(s)

Weiliang Qiu [e-mail]

References

Vittinghoff, E. and Sen, S. and McCulloch, C.E.. Sample size calculations for evaluating mediation. Statistics In Medicine. 2009;28:541-557.

See Also

minEffect.VSMc.cox, powerMediation.VSMc.cox

Examples

  # example in section 6 (page 547) of Vittinghoff et al. (2009).
  # n = 1399
  ssMediation.VSMc.cox(power = 0.7999916, b2 = log(1.5), 
    sigma.m = sqrt(0.25 * (1 - 0.25)), psi = 0.2, corr.xm = 0.3,
    alpha = 0.05, verbose = TRUE)

powerMediation documentation built on March 24, 2021, 1:06 a.m.