May 17, 2023
IMPACT executive committee members, Joan Monin, PhD, MS and Monica Taljaard, PhD, are among authors who have published an article in the International Journal of Epidemiology on planning longitudinal cluster randomized trials (CRT). In this paper, the authors provide examples of correlation parameters, as well as programming code.
Abstract
It is well-known that designing a CRT requires an advance estimate of the intra-cluster correlation coefficient (ICC). In the case of longitudinal CRTs, where outcomes are assessed repeatedly in each cluster over time, estimates for more complex correlation structures are required. Three common types of correlation structures for longitudinal CRTs are exchangeable, nested/block exchangeable and exponential decay correlations—the latter two allow the strength of the correlation to weaken over time. Determining sample sizes under these latter two structures requires advance specification of the within-period ICC and cluster autocorrelation coefficient as well as the intra-individual autocorrelation coefficient in the case of a cohort design. How to estimate these coefficients is a common challenge for investigators. When appropriate estimates from previously published longitudinal CRTs are not available, one possibility is to re-analyse data from an available trial dataset or to access observational data to estimate these parameters in advance of a trial. In this tutorial, the authors demonstrate how to estimate correlation parameters under these correlation structures for continuous and binary outcomes. The authors first introduce the correlation structures and their underlying model assumptions under a mixed-effects regression framework. With practical advice for implementation, they then demonstrate how the correlation parameters can be estimated using examples and we provide programming code in R, SAS, and Stata. An Rshiny app is available that allows investigators to upload an existing dataset and obtain the estimated correlation parameters. They conclude by identifying some gaps in the literature.