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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2405.07722 |
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| _version_ | 1866910506372038656 |
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| author | Ghosh, Biswadeep Dewanji, Anup Das, Sudipta |
| author_facet | Ghosh, Biswadeep Dewanji, Anup Das, Sudipta |
| contents | In survival analysis, frailty variables are often used to model the association in multivariate survival data. Identifiability is an important issue while working with such multivariate survival data with or without competing risks. In this work, we consider bivariate survival data with competing risks and investigate identifiability results with non-parametric baseline cause-specific hazards and different types of Gamma frailty. Prior to that, we prove that, when both baseline cause-specific hazards and frailty distributions are non-parametric, the model is not identifiable. We also construct a non-identifiable model when baseline cause-specific hazards are non-parametric but frailty distribution may be parametric. Thereafter, we consider four different Gamma frailty distributions, and the corresponding models are shown to be identifiable under fairly general assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Identifiability for Bivariate Failure Time Data with Competing Risk: Non-parametric Cause-specific Hazards and Gamma Frailty Ghosh, Biswadeep Dewanji, Anup Das, Sudipta Statistics Theory In survival analysis, frailty variables are often used to model the association in multivariate survival data. Identifiability is an important issue while working with such multivariate survival data with or without competing risks. In this work, we consider bivariate survival data with competing risks and investigate identifiability results with non-parametric baseline cause-specific hazards and different types of Gamma frailty. Prior to that, we prove that, when both baseline cause-specific hazards and frailty distributions are non-parametric, the model is not identifiable. We also construct a non-identifiable model when baseline cause-specific hazards are non-parametric but frailty distribution may be parametric. Thereafter, we consider four different Gamma frailty distributions, and the corresponding models are shown to be identifiable under fairly general assumptions. |
| title | Model Identifiability for Bivariate Failure Time Data with Competing Risk: Non-parametric Cause-specific Hazards and Gamma Frailty |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2405.07722 |