Backward Bayesian Outcome Weighted Learning

Music City Center 

A central objective of precision medicine is learning optimal dynamic treatment regimes (DTRs) from data. Classification-based methods, like outcome weighted learning (OWL) for single-stage and backward OWL (BOWL) for multi-stage problems, leverage machine learning to directly learn optimal DTRs. However, these methods lack a natural way to quantify uncertainty and only use the data from patients whose actual treatment paths align with the optimal decision rule. In this paper, we extend Bayesian OWL – a Bayesian reformulation of OWL – to the multi-stage setting. We call this method backward Bayesian outcome weighted learning (BBOWL). Like BOWL, our method directly learns an optimal DTR via backward induction, and unlike existing methods, our approach propagates uncertainty backward through the DTR-learning process and provides uncertainty quantification of individualized treatment recommendations. Furthermore, our approach leverages the full information contained in the observed data. We present theoretical guarantees of BBOWL and verify its performance via both simulation studies and case study data.

Precision medicine

dynamic treatment regimes

Bayesian statistics 

Biometrics Section