A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies

Avi Kenny; Stephen Olivier; Jianxuan Zang; Jeffrey W. Imai-Eaton; James P. Hughes; Mark J. Siedner

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arXiv · 2408.07738

A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies

Avi Kenny·Stephen Olivier·Jianxuan Zang·Jeffrey W. Imai-Eaton·James P. Hughes·Mark J. Siedner

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Abstract

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.

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Avi Kenny, Stephen Olivier, Jianxuan Zang, Jeffrey W. Imai-Eaton, James P. Hughes, Mark J. Siedner. 2024-08-14. A discrete-time survival model to handle interval-censored covariates, with applications to HIV cohort studies. https://arxiv.org/abs/2408.07738

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