Competing-risk Weibull survival model with multiple causes
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912271045754880 |
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| author | Wang, Kai Mu, Yuqin Zhang, Shenyi Zhang, Zhengjun Ling, Chengxiu |
| author_facet | Wang, Kai Mu, Yuqin Zhang, Shenyi Zhang, Zhengjun Ling, Chengxiu |
| 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_09310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Competing-risk Weibull survival model with multiple causes Wang, Kai Mu, Yuqin Zhang, Shenyi Zhang, Zhengjun Ling, Chengxiu Methodology Statistics Theory 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. |
| title | Competing-risk Weibull survival model with multiple causes |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2503.09310 |