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Bayesian survival analysis using gamma processes with adaptive time partition.
- Source :
- Journal of Statistical Computation & Simulation; Sep2021, Vol. 91 Issue 14, p2937-2952, 16p
- Publication Year :
- 2021
-
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 modelled 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00949655
- Volume :
- 91
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Journal of Statistical Computation & Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 152373825
- Full Text :
- https://doi.org/10.1080/00949655.2021.1912752