Regression analysis of grouped survival data: informative censoring and double sampling

Abstract

To analyze grouped survival data subject to informative censoring, we propose the following two-part model: a logistic regression model for the hazard for failure, given covariates and a logistic regression model for the hazard for informative censoring, given time of failure and covariates. We fit the model to survival data arising from a double sampling design: In a full follow-up (FF) sample subjects are followed after censoring, and in a partial follow-up (PF) sample subjects are not followed after censoring. We illustrate the methodology using data from a study of wound infection in which patients in the PF sample are censored by release from the hospital, whereas patients in the FF sample are followed after hospital release. We discuss identifiability when there is only a PF sample. We also consider how the allocation between the PF and FF samples affects the precision of estimates.