A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo
Document Type
Article
Publication Date
5-30-2020
Publication Title
Statistics in medicine
Abstract
We present a reversible jump Bayesian piecewise log-linear hazard model that extends the Bayesian piecewise exponential hazard to a continuous function of piecewise linear log hazards. A simulation study encompassing several different hazard shapes, accrual rates, censoring proportion, and sample sizes showed that the Bayesian piecewise linear log-hazard model estimated the true mean survival time and survival distributions better than the piecewsie exponential hazard. Survival data from Wake Forest Baptist Medical Center is analyzed by both methods and the posterior results are compared.
First Page
1766
Last Page
1780
DOI
10.1002/sim.8511
Recommended Citation
Chapple, Andrew G.; Peak, Taylor; and Hemal, Ashok, "A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo" (2020). LSU-LCMC Cancer Center Faculty Publications. 40.
https://digitalscholar.lsuhsc.edu/llcc_facpubs/40
10.1002/sim.8511