Set-Valued Dynamic Treatment Regimes for Competing Outcomes

• Activity Number: 152
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Eric Laber*+ and Daniel J. Lizotte and Bradley Ferguson
• Companies: NC State University and University of Waterloo and NC State University
• Keywords: Dynamic treatment regimes ; Personalized medicine ; Preference elicitation ; Mixed integer programming

Abstract

Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each critical decision point, that map up-to-date patient information to a single recommended treatment. Current methods for estimating optimal dynamic treatment regimes require the specification of a single outcome by which the `goodness' of competing dynamic treatment regimes are measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes. For example, often a balance must be struck between treatment effectiveness and side-effect burden. We propose a method for constructing dynamic treatment regimes that accommodates competing outcomes by recommending sets of treatments at each decision point. 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 are not inferior according to any of the competing outcomes.