Restricted mean survival time in cluster randomized trials with a small number of clusters: Improving variance...

In randomized clinical trials with a time-to-event outcome, the intervention effect could be quantified by a difference in restricted mean survival time (ΔRMST) between the intervention and control groups, defined as the expected survival duration gain due to the intervention over a fixed follow-up period. In cluster randomized trials (CRTs), social units are randomized to intervention or control groups; the correlation between survival times of the individuals within the same cluster must be taken into account in the statistical analysis. In a previous work, we proposed the use of pseudo-values regression, based on generalized estimating equations (GEEs), for estimating ΔRMST in CRTs. We showed that this method correctly estimated the ΔRMST and controlled the type I error rate in CRTs with at least 50 clusters. Here, we propose methods for CRTs with a small number of clusters (<50). We evaluated the performance of four bias-corrections of the GEE sandwich variance estimator of the intervention effect. We also considered the use of a Student t distribution as an alternative to the normal distribution of the GEE Wald test statistic for testing the intervention effect and constructing the confidence interval. With a simulation study, assuming proportional or non-proportional hazards, we showed that the Student t distribution outperformed the normal distribution in terms of type I error rate, and the Fay and Graubard bias-corrected variance led to an appropriate type I error rate whatever the number of clusters. Therefore, we recommend the use of the Fay and Graubard variance estimator combined with a Student t distribution for the pseudo-values regression to correctly estimate the variance of the intervention effect. Finally, we provide an illustrative analysis of the DEMETER trial evaluating the use of a specific endotracheal tube for subglottic secretion drainage to prevent ventilator-associated pneumonia, by comparing each of the methods considered.