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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09310 |
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Table of Contents:
- 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.