• Bayes factor;
  • Cox proportional hazards model;
  • Exponential distribution;
  • Partial likelihood;
  • Variable selection

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.