Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain

Abstract

There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high-quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q-learning with policy-search to estimate a high-quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome-dependent follow-up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open-label flexible dosing clinical trials for chronic pain.

Association between dynamic dose increases of buprenorphine for treatment of opioid use disorder and risk of relapse

“…Flexible dosing in trials for substance use disorder treatment is abundant, whereas adaptive randomized trials in which dynamic treatment rules are part of the design are rare [26]. An example of such a trial was one in which patients not responding to buprenorphine were transferred to methadone, resulting in improved outcomes [45].…”

Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models

“…Several methods have been proposed to find dynamic treatment strategies that respect constraints using observational data 6‐10 . Sometimes the constraint is a maximum acceptable level for an adverse outcome rather than a resource constraint, for example, prevalence of a side effect, but the resulting methodology is similar.…”

“…Several methods have been proposed to find dynamic treatment strategies that respect constraints using observational data. [6][7][8][9][10] Sometimes the constraint is a maximum acceptable level for an adverse outcome rather than a resource constraint, for example, prevalence of a side effect, but the resulting methodology is similar. These methods employ the parametric g-formula 11 to estimate effects of candidate treatment strategies and then limit attention to those that satisfy constraints.…”

Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals

“…Policy-search is a common method for estimation of a DTR, and is particularly well-suited to constrained problems (Chakraborty and Moodie, 2013;Wang et al, 2018;Laber et al, 2018).…”