Accelerated Failure Time Models for Competing Risks in a Cluster Weighted Modelling Framework

Abstract

A novel approach for dealing with censored competing risks regression data is proposed. This is implemented by a mixture of accelerated failure time (AFT) models for a competing risks scenario within a cluster-weighted modelling (CWM) framework. Specifically, we make use of the log-normal AFT model here but any commonly used AFT model can be utilized. The alternating expectation conditional maximization algorithm (AECM) is used for parameter estimation and bootstrapping for standard error estimation. Finally, we present our results on some simulated and real competing risks data.