Nonparametric survival analysis using Bayesian Additive Regression Trees (BART)

Rodney A. Sparapani, Brent R. Logan, Robert McCulloch, Purushottam W. Laud

Research output: Contribution to journalArticle

26 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2741-2753
Number of pages13
JournalStatistics in Medicine
Volume35
Issue number16
DOIs
StatePublished - Jul 20 2016
Externally publishedYes

Keywords

  • Cox proportional hazards model
  • ensemble models
  • hematologic malignancy
  • hematopoietic stem cell transplantation
  • Kaplan–Meier estimate
  • marginal dependence functions
  • nonproportional hazards
  • predictive modeling

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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