Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes

Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between treatment effects at the cluster level and the individual level, which requires a clear definition of the estimands under a causal inference framework. For analyzing survival outcomes, it is common to assess the treatment effect by comparing survival functions or restricted mean survival times (RMSTs) between treatment groups. In this article, we formally characterize cluster-level and individual-level treatment effect estimands with right-censored survival outcomes in CRTs and propose doubly robust estimators for targeting such estimands. Under censoring dependent on baseline covariates, our estimators ensure consistency when either the censoring model or the outcome model is correctly specified, but not necessarily both. We explore different modeling options for the censoring and outcome models to estimate the censoring and survival distributions, and investigate a deletion-based jackknife method for variance and interval estimation. Extensive simulations demonstrate that the proposed methods perform adequately in finite samples. Finally, we illustrate our method by analyzing a completed CRT with survival endpoints.