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arXiv · 2008.02204

Bayesian Survival Analysis Using Gamma Processes with Adaptive Time Partition

Yi Li·Sumi Seo·Kyu Ha Lee
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Abstract

In Bayesian semi-parametric analyses of time-to-event data, non-parametric process priors are adopted for the baseline hazard function or the cumulative baseline hazard function for a given finite partition of the time axis. However, it would be controversial to suggest a general guideline to construct an optimal time partition. While a great deal of research has been done to relax the assumption of the fixed split times for other non-parametric processes, to our knowledge, no methods have been developed for a gamma process prior, which is one of the most widely used in Bayesian survival analysis. In this paper, we propose a new Bayesian framework for proportional hazards models where the cumulative baseline hazard function is modeled a priori by a gamma process. A key feature of the proposed framework is that the number and position of interval cutpoints are treated as random and estimated based on their posterior distributions.

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Yi Li, Sumi Seo, Kyu Ha Lee. 2020-08-05. Bayesian Survival Analysis Using Gamma Processes with Adaptive Time Partition. https://arxiv.org/abs/2008.02204

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