Sample size issues for cluster randomized trials with discrete-time survival endpoints

Abstract

With cluster randomized trials complete groups of subjects are randomized to treatment conditions. An important question might be whether and when the subjects experience a particular event, such as smoking initiation or recovery from disease. In the social sciences the timing of such events is often measured in discrete time by using time intervals. At the planning phase of a cluster randomized trial one should decide on the number of clusters and cluster size such that parameters are estimated accurately and sufficient power on the test on treatment effect is achieved. On basis of a simulation study it is concluded that regression coefficients are estimated more accurately than the variance of the random cluster effect. In addition, it is shown that power increases with cluster size and number of clusters, and that a sufficient power cannot always be achieved by using larger cluster sizes at a fixed number of clusters.