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Abstract:
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Bayesian additive regression trees (BART) provide a flexible framework for nonparametric modeling of covariate relationships with their outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, often exceeding that of its competitors and BART software is also readily available for this purpose. In this presentation, I will introduce extending the usefulness of BART in biomedical applications by addressing needs arising from survival analysis. Simulation studies of various regression scenarios, in comparison with long-standing traditional survival analysis methods, will establish the validity of this new approach. Using data from previously published study(ies), I will illustrate the use and some advantages of the proposed method in biomedical investigations.
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