Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes

Yanqing Wang, Yingqi Zhao and Yingye Zheng ()
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Yanqing Wang: Georgia State University
Yingqi Zhao: Fred Hutchinson Cancer Research Center
Yingye Zheng: Fred Hutchinson Cancer Research Center

Statistics in Biosciences, 2022, vol. 14, issue 3, No 10, 564-581

Abstract: Abstract Novel biomarkers, in combination with currently available clinical information, have been sought to enhance clinical decision making in many branches of medicine, including screening, surveillance and prognosis. An individualized clinical decision rule (ICDR) is a decision rule that matches subgroups of patients with tailored medical regimen based on patient characteristics. We proposed new approaches to identify ICDRs by directly optimizing a risk-adjusted clinical benefit function that acknowledges the trade-off between detecting disease and over-treating patients with benign conditions. In particular, we developed a novel plug-in algorithm to optimize the risk-adjusted clinical benefit function, which leads to the construction of both nonparametric and linear parametric ICDRs. In addition, we proposed a novel approach based on the direct optimization of a smoothed ramp loss function to further enhance the robustness of a linear ICDR. We studied the asymptotic theories of the proposed estimators. Simulation results demonstrated good finite sample performance for the proposed estimators and improved clinical utilities when compared to standard approaches. The methods were applied to a prostate cancer biomarker study.

Keywords: Biomarker; False positive fraction; Machine learning; True positive fraction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12561-022-09343-9

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