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 (Kโฅ2) 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