Competing-risk Weibull survival model with multiple causes
Abstract
The failure of a system can result from the simultaneous effects of multiple causes, where assigning a specific cause may be inappropriate or unavailable. Examples include contributing causes of death in epidemiology and the aetiology of neurodegenerative diseases like Alzheimer's. We propose a parametric Weibull accelerated failure time model for multiple causes, incorporating a data-driven, individualized, and time-varying winning probability (relative importance) matrix. Using maximum likelihood estimation and the expectation-maximization (EM) algorithm, our approach enables simultaneous estimation of regression coefficients and relative cause importance, ensuring consistency and asymptotic normality. A simulation study and an application to Alzheimer's disease demonstrate its effectiveness in addressing cause-mixture problems and identifying informative biomarker combinations, with comparisons to Weibull and Cox proportional hazards models.
Cite
@article{arxiv.2503.09310,
title = {Competing-risk Weibull survival model with multiple causes},
author = {Kai Wang and Yuqin Mu and Shenyi Zhang and Zhengjun Zhang and Chengxiu Ling},
journal= {arXiv preprint arXiv:2503.09310},
year = {2025}
}