title: Set-valued dynamic treatment regimes for competing outcomes.
abstract: Dynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q-learning, require the specification of a single outcome by which the "goodness" of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.
authors.affiliation: Department of Statistics, NC State University, Raleigh, North Carolina 27695, U.S.A.
authors.forename: Eric B
authors.initials: EB
authors.lastname: Laber
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Lizotte
authors.forename: Bradley
authors.initials: B
authors.lastname: Ferguson
mesh_terms: D000465:Algorithms; D014150:Antipsychotic Agents / Q000008:administration & dosage / Q000009:adverse effects / Q000627:therapeutic use; D015992:Body Mass Index; D002985:Clinical Protocols; D002986:Clinical Trials as Topic / Q000379:methods*; D003657:Decision Making; D006801:Humans; D015233:Models, Statistical; D012559:Schizophrenia / Q000188:drug therapy; D016896:Treatment Outcome
chemical_list: D014150:Antipsychotic Agents
keywords: Competing outcomes; Composite outcomes; Dynamic treatment regimes; Personalized medicine; Preference elicitation