Bayesian Information Criterion for Censored Survival Models

Bayesian Information Criterion for Censored Survival Models Summary. We investigate the Bayesian Information Criterion (BIG) for variable selection in models for censored survival data. Kass and Wasserman (1995, Journal of the American Statistical Association90, 928–934) showed that BIG provides a close approximation to the Bayes factor when a unit‐information prior on the parameter space is used. We propose a revision of the penalty term in BIG so that it is defined in terms of the number of uncensored events instead of the number of observations. For a simple censored data model, this revision results in a better approximation to the exact Bayes factor based on a conjugate unit‐information prior. In the Cox proportional hazards regression model, we propose defining BIG in terms of the maximized partial likelihood. Using the number of deaths rather than the number of individuals in the BIC penalty term corresponds to a more realistic prior on the parameter space and is shown to improve predictive performance for assessing stroke risk in the Cardiovascular Health Study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Bayesian Information Criterion for Censored Survival Models

Biometrics, Volume 56 (1) – Mar 1, 2000

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Publisher
Wiley
Copyright
Copyright © 2000 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/j.0006-341X.2000.00256.x
Publisher site
See Article on Publisher Site

Abstract

Summary. We investigate the Bayesian Information Criterion (BIG) for variable selection in models for censored survival data. Kass and Wasserman (1995, Journal of the American Statistical Association90, 928–934) showed that BIG provides a close approximation to the Bayes factor when a unit‐information prior on the parameter space is used. We propose a revision of the penalty term in BIG so that it is defined in terms of the number of uncensored events instead of the number of observations. For a simple censored data model, this revision results in a better approximation to the exact Bayes factor based on a conjugate unit‐information prior. In the Cox proportional hazards regression model, we propose defining BIG in terms of the maximized partial likelihood. Using the number of deaths rather than the number of individuals in the BIC penalty term corresponds to a more realistic prior on the parameter space and is shown to improve predictive performance for assessing stroke risk in the Cardiovascular Health Study.

Journal

BiometricsWiley

Published: Mar 1, 2000

References

  • Bayes factors
    Kass, Kass; Raftery, Raftery
  • Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke
    Volinsky, Volinsky; Madigan, Madigan; Raftery, Raftery; Kronmal, Kronmal

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