Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees. Stat Methods Med Res 2019 Apr;28(4):1079-1093
Date
12/20/2017Pubmed ID
29254443Pubmed Central ID
PMC6028324DOI
10.1177/0962280217746191Scopus ID
2-s2.0-85043704690 (requires institutional sign-in at Scopus site) 39 CitationsAbstract
Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.
Author List
Logan BR, Sparapani R, McCulloch RE, Laud PWAuthors
Purushottam W. Laud PhD Adjunct Professor in the Data Science Institute department at Medical College of WisconsinBrent R. Logan PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
Rodney Sparapani PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AlgorithmsBayes Theorem
Decision Making
Models, Statistical
Regression Analysis
Research Design
Uncertainty
