Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/power_VSMc_poisson.R
Calculate sample size for testing mediation effect in poisson regression based on Vittinghoff, Sen and McCulloch's (2009) method.
1 2 3 4 5 6 7 8 9 | ssMediation.VSMc.poisson(power,
b2,
sigma.m,
EY,
corr.xm,
n.lower = 1,
n.upper = 1e+30,
alpha = 0.05,
verbose = TRUE)
|
power |
power for testing b_2=0 for the poisson regression \log(E(Y_i))=b0+b1 x_i + b2 m_i. |
b2 |
regression coefficient for the mediator m in the poisson regression \log(E(Y_i))=b0+b1 x_i + b2 m_i. |
sigma.m |
standard deviation of the mediator. |
EY |
the marginal mean of the outcome. |
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. |
The test is for testing the null hypothesis b_2=0 versus the alternative hypothesis b_2\neq 0 for the poisson regressions:
\log(E(Y_i))=b_0+b_1 x_i + b_2 m_i
Vittinghoff et al. (2009) showed that for the above poisson 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(E(Y_i))=b_0+b_1 x_i + b_2 m_i
The reduced model is
\log(E(Y_i))=b_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.
n |
sample size. |
res.uniroot |
results of optimization to find the optimal sample size. |
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.
Weiliang Qiu stwxq@channing.harvard.edu
Vittinghoff, E. and Sen, S. and McCulloch, C.E.. Sample size calculations for evaluating mediation. Statistics In Medicine. 2009;28:541-557.
minEffect.VSMc.poisson
,
powerMediation.VSMc.poisson
1 2 3 4 5 | # example in section 5 (page 546) of Vittinghoff et al. (2009).
# n = 1239
ssMediation.VSMc.poisson(power = 0.7998578, b2 = log(1.35),
sigma.m = sqrt(0.25 * (1 - 0.25)), EY = 0.5, corr.xm = 0.5,
alpha = 0.05, verbose = TRUE)
|
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