Statistical Inference in Dynamic Treatment Regimes

TL;DR

This paper reviews statistical methods for dynamic treatment regimes, discusses challenges like nonregularity in inference, and proposes adaptive confidence intervals, illustrated with ADHD treatment data.

Contribution

It introduces a locally consistent adaptive confidence interval for parameters in optimal dynamic treatment regimes, addressing nonregularity issues.

Findings

Nonregularity affects asymptotic bias and distribution sensitivity.

Proposed ACI provides more reliable inference in complex regimes.

Application to ADHD data demonstrates practical utility.

Abstract

Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review an interesting challenge, that of nonregularity that often arises in this area. By nonregularity, we mean the parameters indexing the optimal dynamic treatment regime are nonsmooth functionals of the underlying generative distribution. A consequence is that no regular or asymptotically unbiased estimator of these parameters exists. Nonregularity arises in inference for parameters in the optimal dynamic treatment regime; we illustrate the effect of nonregularity on asymptotic bias and via sensitivity of asymptotic, limiting, distributions to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Interventions for Children with ADHD study as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.