Bayes factors for choosing among six common survival models
A super model that includes proportional hazards, proportional odds, accelerated failure time, accelerated hazards, and extended hazards models, as well as the model proposed in Diao et al. (Biometrics 69(4):840–849, 2013) accounting for crossed survival as special cases is proposed for the purpose of testing and choosing among these popular semiparametric models. Efficient methods for fitting and computing fast, approximate Bayes factors are developed using a nonparametric baseline survival function based on a transformed Bernstein polynomial. All manner of censoring is accommodated including right, left, and interval censoring, as well as data that are observed exactly and mixtures of all of these; current status data are included as a special case. The method is tested on simulated data and two real data examples. The approach is easily carried out via a new function in the spBayesSurv R package.
KeywordsInterval censoring Model choice Bernstein polynomial Bayes factor
- Cox DR (1992) Regression models and life-tables. In: Breakthroughs in statistics. Springer, New York, NY, pp 527–541Google Scholar
- Zellner A (1983) Applications of Bayesian analysis in econometrics. J R Stat Soc Ser D (Stat) 32:23–34Google Scholar
- Zhou H, Hanson T (2015) Bayesian spatial survival models. In: Nonparametric Bayesian inference in biostatistics. Springer, Cham, pp 215–246Google Scholar