Dynamic treatment regime characterization via value function surrogate with an application to partial compliance

Freeman, Nikki L. B.; Browder, Sydney E.; McGinigle, Katharine L.; Kosorok, Michael R.

doi:10.48550/arxiv.2212.00650

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“…Discussion about how to account for sources of uncertainty is also emphasized, in contrast to [10]. Related work using GPs as surrogates for the value function can be found in [8]. With these nuanced differences in mind, we now examine the inferential problem more closely.…”

Section: Identifying Optimal Dtrs Via Gaussian Process Emulationmentioning

confidence: 99%

Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models

et al. 2022

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Summary Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of uncertainty, including at the individual level. In this work, we extend the use of recently developed Bayesian methods for Marginal Structural Models to arrive at inference of DTRs. We do this (i) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then (ii) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from nonparametric prior assumptions on the observational world data-generating process. Our approach relies on Bayesian semiparametric inference, where inference about a finite-dimensional parameter is made all while working within an infinite-dimensional space of distributions. We further study Bayesian inference of DTRs in the double robust setting by using posterior predictive inference and the nonparametric Bayesian bootstrap. The proposed methods allow for uncertainty quantification at the individual level, thereby enabling personalized decision-making. We examine the performance of these methods via simulation and demonstrate their utility by exploring whether to adapt HIV therapy to a measure of patients’ liver health, in order to minimize liver scarring.

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Section: Identifying Optimal Dtrs Via Gaussian Process Emulationmentioning

confidence: 99%