ContICC

This function calculates the within-period, between-period, and within-individual correlation parameters for continuous outcomes in a stepped wedge CRT

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

Jiachen Chen

swdpwr

Power Calculation for Stepped Wedge Cluster Randomized Trials

ContICC function

<dl><dt>type</dt>
<dd>choose the study type, specify type="cohort" for closed cohort study and type="cross-sectional" for cross-sectional study, default is "cross-sectional"</dd>
<dt>sigma2</dt>
<dd>marginal variance of the outcom, default is 1</dd>
<dt>sigma_b</dt>
<dd>variance of the between-cluster random effect, default is 0</dd>
<dt>sigma_c</dt>
<dd>variance of the cluster-by-time interaction random effect, default is 0</dd>
<dt>sigma_pi</dt>
<dd>variance of the random effect for repeated measures of one individual, this parameter should not be specified for cross-sectional studies, default is NA</dd></dl>

Arguments

A function for calculating ICCs for continuous outcomes given random effects variances — ContICC

<dl>

<dt>type</dt>
<dd>choose the study type, specify type="cohort" for closed cohort study and type="cross-sectional" for cross-sectional study, default is "cross-sectional"</dd>


<dt>sigma2</dt>
<dd>marginal variance of the outcom, default is 1</dd>


<dt>sigma_b</dt>
<dd>variance of the between-cluster random effect, default is 0</dd>


<dt>sigma_c</dt>
<dd>variance of the cluster-by-time interaction random effect, default is 0</dd>


<dt>sigma_pi</dt>
<dd>variance of the random effect for repeated measures of one individual, this parameter should not be specified for cross-sectional studies, default is NA</dd>

</dl>

ContICC: A function for calculating ICCs for continuous outcomes given random effects variances

Description

Usage

Value

Arguments

Examples