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Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke
Author(s) -
Volinsky Chris T.,
Madigan David,
Raftery Adrian E.,
Kronmal Richard A.
Publication year - 1997
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00082
Subject(s) - leaps , hazard , context (archaeology) , bayesian probability , proportional hazards model , variable (mathematics) , statistics , model selection , hazard ratio , feature selection , computer science , econometrics , mathematics , confidence interval , artificial intelligence , economics , paleontology , mathematical analysis , chemistry , organic chemistry , financial economics , biology
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.

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