Power analysis for cluster randomized trials with continuous co-primary endpoints

TL;DR

This paper develops a statistical method for power analysis in cluster randomized trials with multiple continuous co-primary endpoints, accounting for complex correlation structures, to improve sample size determination.

Contribution

It introduces a multivariate linear mixed model framework for continuous endpoints, deriving the joint distribution of treatment effects for power analysis in CRTs with multiple co-primary endpoints.

Findings

Predicted power aligns well with empirical power in simulations.

Method accommodates unequal cluster sizes through approximation.

Application to real CRT demonstrates practical utility.

Abstract

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Previous reviews have shown that co-primary endpoints are common in pragmatic trials but infrequently recognized in sample size or power calculations. While methods for power analysis based on $K$ ($K\geq 2$) binary co-primary endpoints are available for CRTs, to our knowledge, methods for continuous co-primary endpoints are not yet available. Assuming a multivariate linear mixed model that accounts for multiple types of intraclass correlation coefficients (endpoint-specific ICCs, intra-subject ICCs and inter-subject between-endpoint ICCs) among the observations in each cluster, we derive the closed-form joint distribution of $K$ treatment effect estimators to facilitate sample size and power determination with different types of null hypotheses under equal cluster sizes. We characterize the relationship between the power of each test and different types of correlation parameters. We further relax the equal cluster size assumption and approximate the joint distribution of the $K$ treatment effect estimators through the mean and coefficient of variation of cluster sizes. Our simulation studies with a finite number of clusters indicate that the predicted power by our method agrees well with the empirical power, when the parameters in the multivariate linear mixed model are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.