Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.

ABSTRACT: 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 ind

SUBMITTER: Logan BR 

PROVIDER: S-EPMC6028324 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature