title: Characterizing expected benefits of biomarkers in treatment selection.
abstract: Biomarkers associated with heterogeneity in subject 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 using the marker on the rate of targeted disease and on the cost associated with treatment. We propose an expected benefit measure that incorporates both effects to quantify a marker'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 cost of treatment. In addition, we establish upper and lower bounds on the expected benefit for a perfect treatment-selection rule which provides the basis for a standardized expected benefit measure. We develop model-based estimators for these measures in a randomized trial setting and evaluate their asymptotic properties. An adaptive bootstrap confidence interval is proposed for inference in the presence of non-regularity. Alternative estimators robust to risk model misspecification are also investigated. We illustrate our methods using the Diabetes Control and Complications Trial where we evaluate the expected benefit of baseline hemoglobin A1C in selecting diabetes treatment.
authors.affiliation: Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., Seattle WA, 98109, USA yhuang@fhcrc.org.
authors.forename: Ying
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC,27695-8203, USA.
authors.forename: Eric B
authors.initials: EB
authors.lastname: Laber
authors.affiliation: Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., Seattle WA, 98109, USA.
authors.forename: Holly
authors.initials: H
authors.lastname: Janes
mesh_terms: D015415:Biomarkers; D000066491:Clinical Decision-Making / Q000379:methods*; D003920:Diabetes Mellitus / Q000628:therapy; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D015415:Biomarkers; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: Adaptive bootstrap; Biomarker; Expected benefit; Potential outcomes; Treatment selection