A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866918045742530560 |
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| author | Kenny, Avi Olivier, Stephen Zang, Jianxuan Imai-Eaton, Jeffrey W. Hughes, James P. Siedner, Mark J. |
| author_facet | Kenny, Avi Olivier, Stephen Zang, Jianxuan Imai-Eaton, Jeffrey W. Hughes, James P. Siedner, Mark J. |
| contents | Methods are lacking to handle the problem of survival analysis in the presence of an interval-censored covariate, specifically the case in which the conditional hazard of the primary event of interest depends on the occurrence of a secondary event, the observation time of which is subject to interval censoring. We propose and study a flexible class of discrete-time parametric survival models that handle the censoring problem through simultaneous modeling of the interval-censored secondary event, the outcome, and the censoring mechanism. We apply this model to the research question that motivated the methodology, estimating the effect of HIV status on all-cause mortality in a prospective cohort study in South Africa. Our model has applicability for many open questions, including estimating the impact of policy decisions on population level HIV-related outcomes and determining causes of morbidity and mortality for which the HIV positive population may be at increased risk. Examples include determining how the large-scale transition from efavirenz-based to dolutegravir-based first-line ART impacted mortality for people living with HIV and determining whether HIV status is associated with increased risk of stroke, diabetes, hypertension, and other non-communicable diseases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies Kenny, Avi Olivier, Stephen Zang, Jianxuan Imai-Eaton, Jeffrey W. Hughes, James P. Siedner, Mark J. Methodology Methods are lacking to handle the problem of survival analysis in the presence of an interval-censored covariate, specifically the case in which the conditional hazard of the primary event of interest depends on the occurrence of a secondary event, the observation time of which is subject to interval censoring. We propose and study a flexible class of discrete-time parametric survival models that handle the censoring problem through simultaneous modeling of the interval-censored secondary event, the outcome, and the censoring mechanism. We apply this model to the research question that motivated the methodology, estimating the effect of HIV status on all-cause mortality in a prospective cohort study in South Africa. Our model has applicability for many open questions, including estimating the impact of policy decisions on population level HIV-related outcomes and determining causes of morbidity and mortality for which the HIV positive population may be at increased risk. Examples include determining how the large-scale transition from efavirenz-based to dolutegravir-based first-line ART impacted mortality for people living with HIV and determining whether HIV status is associated with increased risk of stroke, diabetes, hypertension, and other non-communicable diseases. |
| title | A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies |
| topic | Methodology |
| url | https://arxiv.org/abs/2408.07738 |