title: 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 procedure with the "oracle" procedure, defined as the one that makes treatment recommendations based on the true parameter values as opposed to their estimates. We also look at bias and mean squared error of the individual parameter-estimates as secondary metrics. Finally, we analyze the STAR*D data using the proposed method.
authors.affiliation: Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore. bibhas.chakraborty@duke-nus.edu.sg.
authors.forename: Bibhas
authors.initials: B
authors.lastname: Chakraborty
authors.affiliation: Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore.
authors.forename: Palash
authors.initials: P
authors.lastname: Ghosh
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
authors.forename: Erica E M
authors.initials: EE
authors.lastname: Moodie
authors.affiliation: Office of Clinical Sciences, Duke-National University of Singapore Medical School, Singapore.
authors.forename: A John
authors.initials: AJ
authors.lastname: Rush
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D003865:Major Depressive Disorder / Q000628:therapy*; D006801:Humans; D015233:Models, Statistical; D057285:Precision Medicine; D016032:Randomized Controlled Trials as Topic
keywords: Dynamic treatment regimens; Q-learning; STAR*D; Shared parameters