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Bayes factors for choosing among six common survival models

Author

Listed:
  • Jiajia Zhang

    (University of South Carolina)

  • Timothy Hanson

    (Medtronic Inc.)

  • Haiming Zhou

    (Northern Illinois University)

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.

Suggested Citation

  • Jiajia Zhang & Timothy Hanson & Haiming Zhou, 2019. "Bayes factors for choosing among six common survival models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 361-379, April.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:2:d:10.1007_s10985-018-9429-4
    DOI: 10.1007/s10985-018-9429-4
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    References listed on IDEAS

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