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Stat Biosci. 2016;8:43-65. Epub 2014 Nov 21.

Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective.

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Biostat & Biomath Program, Fred Hutchinson Cancer Center, Seattle, WA 98109 USA.
Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695 USA.


For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient's expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: (i) when biomarker data are available from a randomized trial; and (ii) when biomarker data are available from a cohort or a cross-sectional study, together with external information about a multiplicative treatment effect. We also develop adaptive bootstrap confidence intervals for consistent inference in the presence of nonregularity. The proposed method is used to evaluate the individualized value of the serum creatinine marker in informing treatment decisions for the prevention of renal artery stenosis.


Adaptive bootstrap; Biomarker; Cost-benefit; Semiparametric location-scale model; Subject-specific expected benefit; Treatment selection

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