A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo

被引：3

作者：

Chapple, Andrew G. ^{[1
]}

Peak, Taylor ^{[2
]}

Hemal, Ashok ^{[2
]}

机构：

[1] Louisiana State Univ, Hlth Sci Ctr, Biostat Program, Sch Publ Hlth, New Orleans, LA 70112 USA

[2] Wake Forest Baptist Med Ctr, Dept Urol, Winston Salem, NC USA

来源：

STATISTICS IN MEDICINE | 2020年 / 39卷 / 12期

基金：

美国国家科学基金会;

关键词：

Bayesian methods; Cox models; hazard estimation; reversible jump Markov chain Monte Carlo; survival analysis; SURVIVAL-DATA; OUTCOMES;

D O I：

10.1002/sim.8511

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：1766 / 1780

页数：15

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