Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial.

ABSTRACT: A dynamic treatment regimen consists of decision rules that recommend how to individualize treatment to patients based on available treatment and covariate history. In many scientific domains, these decision rules are shared across stages of intervention. As an illustrative example, we discuss STAR*D, a multistage randomized clinical trial for treating major depression. Estimating these shared decision rules often amounts to estimating parameters indexing the decision rules that are shared across stages. In this article, we propose a novel simultaneous estimation procedure for the shared parameters based on Q-learning. We provide an extensive simulation study to illustrate the merit of the proposed method over simple competitors, in terms of the treatment allocation matching of the procedu

SUBMITTER: Chakraborty B 

PROVIDER: S-EPMC4988949 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature