Bayesian inference for optimal dynamic treatment regimes in practice

Rodriguez Duque Daniel (), Moodie Erica E. M. () and Stephens David A. ()
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Rodriguez Duque Daniel: Department of Epidemiology & Biostatistics, McGill University, Montréal, QC, Canada
Moodie Erica E. M.: Department of Epidemiology & Biostatistics, McGill University, Montréal, QC, Canada
Stephens David A.: Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada

The International Journal of Biostatistics, 2023, vol. 19, issue 2, 309-331

Abstract: In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying optimal DTRs. Recent work has developed Bayesian methods for identifying optimal DTRs in a family indexed by ψ via Bayesian dynamic marginal structural models (MSMs) (Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)); we review the proposed estimation procedure and illustrate its use via the new BayesDTR R package. Although methods in Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. (Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)) can estimate optimal DTRs well, they may lead to biased estimators when the model for the expected outcome if everyone in a population were to follow a given treatment strategy, known as a value function, is misspecified or when a grid search for the optimum is employed. We describe recent work that uses a Gaussian process ( G P ) $(\mathcal{G}\mathcal{P})$ prior on the value function as a means to robustly identify optimal DTRs (Rodriguez Duque D, Stephens DA, Moodie EEM. Estimation of optimal dynamic treatment regimes using Gaussian processes; 2022. Available from: https://doi.org/10.48550/arXiv.2105.12259). We demonstrate how a G P $\mathcal{G}\mathcal{P}$ approach may be implemented with the BayesDTR package and contrast it with other value-search approaches to identifying optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate a standard analysis with these methods, using both the original observed trial data and an additional simulated component to showcase a longitudinal (two-stage DTR) analysis.

Keywords: Bayesian inference, dynamic treatment regimes, precision medicine; R package. (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2022-0073

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