Bayes factors for choosing among six common survival models

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Abstract

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.

Keywords

Interval censoring Model choice Bernstein polynomial Bayes factor 
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Supplementary material

10985_2018_9429_MOESM1_ESM.pdf (179 kb)
Supplementary material 1 (pdf 179 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Epidemiology and BiostatisticsUniversity of South CarolinaColumbiaUSA
  2. 2.Medtronic Inc.MinneapolisUSA
  3. 3.Division of StatisticsNorthern Illinois UniversityDeKalbUSA

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