title: Identifying a set that contains the best dynamic treatment regimes.
abstract: A dynamic treatment regime (DTR) is a treatment design that seeks to accommodate patient heterogeneity in response to treatment. DTRs can be operationalized by a sequence of decision rules that map patient information to treatment options at specific decision points. The sequential, multiple assignment, randomized trial (SMART) is a trial design that was developed specifically for the purpose of obtaining data that informs the construction of good (i.e. efficacious) decision rules. One of the scientific questions motivating a SMART concerns the comparison of multiple DTRs that are embedded in the design. Typical approaches for identifying the best DTRs involve all possible comparisons between DTRs that are embedded in a SMART, at the cost of greatly reduced power to the extent that the number of embedded DTRs (EDTRs) increase. Here, we propose a method that will enable investigators to use SMART study data more efficiently to identify the set that contains the most efficacious EDTRs. Our method ensures that the true best EDTRs are included in this set with at least a given probability. Simulation results are presented to evaluate the proposed method, and the Extending Treatment Effectiveness of Naltrexone SMART study data are analyzed to illustrate its application.
authors.affiliation: Department of Statistics, University of Pennsylvania, Philadelphia, PA 19104, USA and Center for Pharmacoepidemiology Research and Training, University of Pennsylvania, Philadelphia, PA 19104, USA ertefaie@wharton.upenn.edu.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Ertefaie
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Tianshuang
authors.initials: T
authors.lastname: Wu
authors.affiliation: Treatment Research Center and Center for Studies of Addictions, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Kevin G
authors.initials: KG
authors.lastname: Lynch
authors.affiliation: Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Inbal
authors.initials: I
authors.lastname: Nahum-Shani
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016032:Randomized Controlled Trials as Topic; D012107:Research Design
keywords: Double robust; Marginal structural model; Multiple comparisons with the best; SMART designs