Generalized Accelerated Failure Time Models for Recurrent Events

Abstract

For analyzing recurrent event data, we consider a generalization of the classical accelerated failure time model. In the proposed approach, the general function is no longer assumed to be a singleton but allowed to be time-varying. This is in the same spirit as in quantile regression and the counting process techniques can be utilized. Theoretical properties such as consistency and asymptotic normality are obtained. The illustration of the methodology using simulation studies and then the application to the bladder cancer data is also given.

Keywords

• accelerated failure time model
• censored quantile regression
• counting processes
• recurrent events
• time-varying general function