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Nonparametric survival analysis using Bayesian Additive Regression Trees (BART). Stat Med 2016 07 20;35(16):2741-53 PMID: 26854022 PMCID: PMC4899272

Pubmed ID

26854022

Abstract

Bayesian additive regression trees (BART) provide a framework for flexible nonparametric modeling of relationships of covariates to outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, and exceeding that of its competitors. Software is also readily available for such outcomes. In this article, we introduce modeling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis. Simulation studies of one-sample and two-sample scenarios, in comparison with long-standing traditional methods, establish face validity of the new approach. We then demonstrate the model's ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario with crossing survival functions and survival function estimation in a scenario where hazards are multiplicatively modified by a highly nonlinear function of the covariates. Using data from a recently published study of patients undergoing hematopoietic stem cell transplantation, we illustrate the use and some advantages of the proposed method in medical investigations. Copyright © 2016 John Wiley & Sons, Ltd.

Author List

Sparapani RA, Logan BR, McCulloch RE, Laud PW

Authors

Purushottam W. Laud PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Brent R. Logan PhD Director, Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Rodney Sparapani PhD Assistant Professor in the Institute for Health and Equity department at Medical College of Wisconsin




Scopus

2-s2.0-84978975552   7 Citations

MESH terms used to index this publication - Major topics in bold

Bayes Theorem
Humans
Proportional Hazards Models
Regression Analysis
Reproducibility of Results
Software
Survival Analysis
jenkins-FCD Prod-299 9ef562391eceb2b8f95265c767fbba1ce5a52fd6