Volinsky, Chris T.; Madigan, David; Raftery, Adrian E.; Kronmal, Richard A. Bayesian model averaging in proportional hazard models: Assessing the risk of a stroke. (English) Zbl 0903.62093 J. R. Stat. Soc., Ser. C 46, No. 4, 433-448 (1997). Summary: In the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for strokes, we apply Bayesian model averaging to the selection of variables in Cox proportional hazard models. We use an extension of the leaps-and-bounds algorithm for locating the models that are to be averaged over and make available S-PLUS software to implement the methods. Bayesian model averaging provides a posterior probability that each variable belongs in the model, a more directly interpretable measure of variable importance than a \(P\)-value. \(P\)-values from models preferred by stepwise methods tend to overstate the evidence for the predictive value of a variable and do not account for model uncertainty. We introduce the partial predictive score to evaluate predictive performance. For the Cardiovascular Health Study, Bayesian model averaging predictively outperforms standard model selection and does a better job of assessing who is at high risk for a stroke. Cited in 32 Documents MSC: 62P10 Applications of statistics to biology and medical sciences; meta analysis 62F15 Bayesian inference Keywords:cardiovascular health study; model uncertainty; Stroke risk factors; variable selection; Bayesian model averaging; S-PLUS software Software:GLIM; S-PLUS PDFBibTeX XMLCite \textit{C. T. Volinsky} et al., J. R. Stat. Soc., Ser. C 46, No. 4, 433--448 (1997; Zbl 0903.62093) Full Text: DOI