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powerMediation (version 0.3.4)

ssLong.multiTime: Sample size calculation for testing if mean changes for 2 groups are the same or not for longitudinal study with more than 2 time points

Description

Sample size calculation for testing if mean changes for 2 groups are the same or not for longitudinal study with more than 2 time points.

Usage

ssLong.multiTime(es, power, nn, sx2, rho = 0.5, alpha = 0.05)

Arguments

es

effect size

power

power

nn

number of observations per subject

sx2

within subject variance

rho

within subject correlation

alpha

type I error rate

Value

subject per group

Details

We are interested in comparing the slopes of the 2 groups \(A\) and \(B\): $$ \beta_{1A} = \beta_{1B} $$ where $$ Y_{ijA}=\beta_{0A}+\beta_{1A} x_{jA} + \epsilon_{ijA}, j=1, \ldots, nn; i=1, \ldots, m $$ and $$ Y_{ijB}=\beta_{0B}+\beta_{1B} x_{jB} + \epsilon_{ijB}, j=1, \ldots, nn; i=1, \ldots, m $$

The sample size calculation formula is (Equation on page 30 of Diggle et al. (1994)): $$ m=\frac{2\left(Z_{1-\alpha}+z_{power}\right)^2 \left(1-\rho\right)}{ nn s_x^2 es^2} $$ where \(es=d/\sigma\), \(d\) is the meaninful differnce of interest, \(sigma^2\) is the variance of the random error, \(\rho\) is the within-subject correlation, and \(s_x^2\) is the within-subject variance.

References

Diggle PJ, Liang KY, and Zeger SL (1994). Analysis of Longitundinal Data. page 30. Clarendon Press, Oxford

See Also

powerLong.multiTime

Examples

Run this code
# NOT RUN {
# subject per group = 196
ssLong.multiTime(es=0.5/10, power=0.8, nn=3, sx2=4.22, rho = 0.5, alpha=0.05)
# }

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