|Title||Set-valued dynamic treatment regimes for competing outcomes.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Laber, Eric B., Daniel J. Lizotte, and Bradley Ferguson|
|Date Published||2014 Mar|
|Keywords||Algorithms, Antipsychotic Agents, Body Mass Index, Clinical Protocols, Clinical Trials as Topic, Decision Making, Humans, Models, Statistical, Schizophrenia, Treatment Outcome|
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.
|Original Publication||Set-valued dynamic treatment regimes for competing outcomes.|
|PubMed Central ID||PMC3954452|
|Grant List||N01MH90001 / MH / NIMH NIH HHS / United States |
P01 CA142538 / CA / NCI NIH HHS / United States
Set-valued dynamic treatment regimes for competing outcomes.