JSM 2005 - Toronto

Abstract #304321

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 123
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Health Policy Statistics
Abstract - #304321
Title: Optimal Bayesian Design for Patient Selection in a Clinical Trial
Author(s): Manuela Buzoianu*+ and Joseph B. Kadane
Companies: Carnegie Mellon University and Carnegie Mellon University
Address: Carnegie Mellon University, Dept. of Statistics, Pittsburgh, PA, 15213, United States
Keywords: clinical trial ; optimal design ; medical decisions ; Bayesian inference ; ethical concerns
Abstract:

Recently, many experimental design problems in clinical studies and other areas have shown difficulties in obtaining optimal design solutions. In a clinical trial, we determine the selection of patients for an invasive diagnostic test using a Bayesian experimental design. In conducting this experiment, we describe the available medical decisions, gather the experimental data by verifying patient disease status and their characteristics, perform statistical analysis for these data, and---based on this analysis---choose the best medical action for any future patient. The optimal design describes the best scientific set of experimental patients. It is presented in a decision theoretic framework by using a problem-specific utility function. Greedy and backward selection methods are developed to overcome the computational problems due to a discrete high-dimensional design space. A consequence of the optimal design is that some of the selected experimental subjects get assignments that do not maximize patient's utility. A constraint is imposed on the design by limiting assignments to patients who meet a criteria dictated by ethical concerns.

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Revised March 2005