ssMediation.VSMc: Sample size for testing mediation effect in linear regression based on Vittinghoff, Sen and McCulloch's (2009) method

Description

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

Usage

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

Arguments

power

power for testing b_2=0 for the linear regression y_i=b0+b1 x_i + b2 m_i + ε_i, ε_i\sim N(0, σ_e^2).

b2

regression coefficient for the mediator m in the linear regression y_i=b0+b1 x_i + b2 m_i + ε_i, ε_i\sim N(0, σ_e^2).

sigma.m

standard deviation of the mediator.

sigma.e

standard deviation of the random error term in the linear regression y_i=b0+b1 x_i + b2 m_i + ε_i, ε_i\sim N(0, σ_e^2).

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 linear regressions:

y_i=b_0+b_1 x_i + b_2 m_i + ε_i, ε_i\sim N(0, σ^2_{e})

Vittinghoff et al. (2009) showed that for the above linear 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

y_i=b_0+b_1 x_i + b_2 m_i + ε_i, ε_i\sim N(0, σ^2_{e}).

The reduced model is

y_i=b_0+b_1 x_i + ε_i, ε_i\sim N(0, σ^2_{e}).

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. Code for one-sided tests will be added later.

Author(s)

Weiliang Qiu stwxq@channing.harvard.edu

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, powerMediation.VSMc

Examples

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  # example in section 3 (page 544) of Vittinghoff et al. (2009).
  # n=863
  ssMediation.VSMc(power = 0.80, b2 = 0.1, sigma.m = 1, sigma.e = 1, 
    corr.xm = 0.3, alpha = 0.05, verbose = TRUE)

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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