Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/powerLongitudinal.R
Sample size calculation for testing if mean changes for 2 groups are the same or not for longitudinal study with 2 time point.
1 2 3 4 |
es |
effect size of the difference of mean change. |
rho |
correlation coefficient between baseline and follow-up values within a treatment group. |
alpha |
Type I error rate. |
power |
power for testing for difference of mean changes. |
The sample size formula is based on Equation 8.30 on page 335 of Rosner (2006).
n=\frac{2σ_d^2 (Z_{1-α/2} + Z_{power})^2}{δ^2}
where σ_d = σ_1^2+σ_2^2-2ρσ_1σ_2, δ=|μ_1 - μ_2|, μ_1 is the mean change over time t in group 1, μ_2 is the mean change over time t in group 2, σ_1^2 is the variance of baseline values within a treatment group, σ_2^2 is the variance of follow-up values within a treatment group, ρ is the correlation coefficient between baseline and follow-up values within a treatment group, and Z_u is the u-th percentile of the standard normal distribution.
We wish to test μ_1 = μ_2.
When σ_1=σ_2=σ, then formula reduces to
n=\frac{4(1-ρ)(Z_{1-α/2}+Z_{β})^2}{d^2}
where d=δ/σ.
required sample size per group
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
Rosner, B. Fundamentals of Biostatistics. Sixth edition. Thomson Brooks/Cole. 2006.
ssLongFull
, powerLong
,
powerLongFull
.
1 2 3 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.