A test for informative censoring in clustered survival data

Abstract

Frailty models are frequently used to analyse clustered survival data. The assumption of non-informative censoring is commonly used by these models, even though it may not be true in many situations. This article proposes a test for this assumption. It uses the estimated correlation between two types of martingale residuals, one from a model for failure and the other from a model for censoring. It distinguishes two types of censoring, namely withdrawal and the end of the study. Simulation studies show that the proposed test works well under various scenarios. For illustration, the test is applied to a data set for kidney disease patients from multiple dialysis centres.