Journal
BIOMETRICS
Volume 79, Issue 2, Pages 1293-1305Publisher
WILEY
DOI: 10.1111/biom.13692
Keywords
coefficient of variation; general linear hypothesis; intersection-union test; multivariate linear mixed model; sample size determination; unequal cluster size
Ask authors/readers for more resources
Pragmatic trials in healthcare interventions often use cluster randomization, but methods for determining sample size and power for continuous coprimary endpoints are lacking. We propose a method based on multivariate linear mixed models to address this gap and demonstrate its effectiveness through simulation studies.
Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K (K >= 2$K\ge 2$) binary coprimary endpoints are available for cluster randomized trials (CRTs), to our knowledge, methods for continuous coprimary endpoints are not yet available. Assuming a multivariate linear mixed model (MLMM) that accounts for multiple types of intraclass correlation coefficients 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 MLMM are estimated via the expectation-maximization algorithm. An application to a real CRT is presented to illustrate the proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available