Bayesian Outcome Weighted Learning

Sophia Yazzourh; Nikki L. B. Freeman

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arXiv · 2406.11573

Bayesian Outcome Weighted Learning

Sophia Yazzourh·Nikki L. B. Freeman

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Abstract

One of the primary goals of statistical precision medicine is to learn optimal individualized treatment rules (ITRs). The classification-based, or machine learning-based, approach to estimating optimal ITRs was first introduced in outcome-weighted learning (OWL). OWL recasts the optimal ITR learning problem into a weighted classification problem, which can be solved using machine learning methods, e.g., support vector machines. In this paper, we introduce a Bayesian formulation of OWL. Starting from the OWL objective function, we generate a pseudo-likelihood which can be expressed as a scale mixture of normal distributions. A Gibbs sampling algorithm is developed to sample the posterior distribution of the parameters. 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 the performance of our method through several simulation studies.

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BibTeX RIS

Sophia Yazzourh, Nikki L. B. Freeman. 2024-06-17. Bayesian Outcome Weighted Learning. https://arxiv.org/abs/2406.11573

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