Statistical issues in survival analysis (Part XIII)
August 30, 2023 In this article that appeared in a special issue in the Biometrical Journal, Chen et al describe how restricted mean survival time (RMST), an alternative representation of survival time, can be adapted to have clustering. They were motivated by multicenter randomized trials or cluster randomized trials. They employed a generalized estimating equation approach (GEE) with a working independence assumption and inverse probability of censoring weights (IPCW), They specifically focused on an approach, the RMST regression method by Zhong and Schaubel (2022) (abbreviated as the ZS method), which assumes independent and identically distributed survival data, and which as they stated cannot be directly applied without modifications. Their proposition was to modify the ZS approach and also to cultivate bias-corrected sandwich variance estimators all for their RMST regression with clustering approach. They employed the GEE with IPCW following the Robbins and Finkelstein (2000) approach. They demonstrated four difference bias correction estimators that have already been published by other authors by testing in simulations to see if they would work with clustered data instead of i.i.d. survival data and also IPCW. When they employed their simulations, they used a generalized linear model with frailty to generate correlated survival data. The results showed that accounting for clustering was important as compared to using a model that doesn’t handle clustering when the data clearly has clustering. They found that to achieve nominal confidence interval coverage with a small number of clusters, that their results showed the the MBN-type variance estimator for the intercept and