title: Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.
abstract: In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average.Q- andA-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.
authors.affiliation: Biostatistician, Duke Clinical Research Institute, Durham, North Carolina 27701, USA ( phillip.schulte@duke.edu ).
authors.forename: Phillip J
authors.initials: PJ
authors.lastname: Schulte
authors.affiliation: Gertrude M. Cox Distinguished Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( tsiatis@ncsu.edu ).
authors.forename: Anastasios A
authors.initials: AA
authors.lastname: Tsiatis
authors.affiliation: Assistant Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( eblaber@ncsu.edu ).
authors.forename: Eric B
authors.initials: EB
authors.lastname: Laber
authors.affiliation: William Neal Reynolds Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( davidian@ncsu.edu ).
authors.forename: Marie
authors.initials: M
authors.lastname: Davidian
keywords: Advantage learning; bias-variance tradeoff; model misspecification; personalized medicine; potential outcomes; sequential decision making