Bayesian outcome weighted learning for estimating sparse optimal individualized treatment rules

Oregon Convention Center 

One of the primary goals of statistical precision medicine is to learn optimal individualized treatment rules (ITRs). The classification-based approach to estimating optimal ITRs was first introduced in outcome-weighted learning (OWL), which recasts the optimal ITR learning problem into a weighted classification problem. In this presentation, we introduce a Bayesian formulation of OWL. Starting from the OWL objective function, we generate an empirical likelihood that can be expressed as a scale mixture of normal distributions. A Gibbs sampling algorithm is developed for estimation. In addition to providing a strategy for learning an optimal ITR, Bayesian OWL provides a natural, probabilistic approach to estimate uncertainty in ITR treatment recommendations themselves. We demonstrate our method through several simulation studies and a healthcare application.

Precision medicine

Individualized treatment rule

Bayesian machine learning 

Biometrics Section