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Activity Number: 12
Type: Topic Contributed
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #311741
Title: Characterizing Expected Benefits of Biomarkers in Treatment Selection
Author(s): Ying Huang*+ and Eric B. Laber and Holly Janes
Companies: Fred Hutchinson Cancer Research Center and North Carolina State University and Fred Hutchinson Cancer Research Center
Keywords: Adaptive bootstrap ; Biomarker ; Expected Benefit ; Potential Outcomes ; Treatment Selection
Abstract:

Biomarkers associated with heterogeneity in patient responses to treatment hold potential for treatment selection. In practice, the decision regarding whether to adopt a treatment-selection marker depends on the effect of treatment selection on the rate of targeted disease and on the burden associated with treatment. We propose an expected benefit measure that incorporates both effects to quantify a biomarker's treatment-selection capacity. This measure builds upon an existing decision-theoretic framework, but is expanded to account for the fact that optimal treatment absent marker information varies with the burden of treatment. We establish upper and lower bounds for a perfect treatment-selection marker based on a potential outcomes framework, which provides a basis for standardizing markers' expected benefit. Different strategies for estimation and inference in a randomized trial setting are investigated through numerical studies and in a real example from the Diabetes Control and Complications Trial.


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