Work

Two‐Step Error‐Controlling Classifiers With Application to Cost‐Effective Disease Diagnosis

Year: 2026

Type: article

Abstract: Accurate classifiers that use novel biomarkers and readily available predictors significantly enhance decision-making in various clinical scenarios, such as assessing the need for biopsies in cancer diagnosis. When classification performance is limited, a decision framework can be applied to rule in or rule out invasive diagnostic procedures while incorporating a neutral zone for indeterminate classifications. Building on this framework, we propose a new family of two-step classifiers that selectively use costly biomarker testing for a targeted subset of individuals undergoing multiple evaluations. The optimal solution expands upon the Neyman-Pearson Lemma, highlighting a vital trade-off between the costs of expensive biomarker measurements and improving classification performance while minimizing uncertainty in the decision process. We demonstrate the practical utility of our approach through a biomarker study focused on prostate cancer diagnosis.

Source: Statistics in Medicine

DOI: 10.1002/sim.70498

Authors: Kehao Zhu, Kwun Chuen Gary Chan, Ying‐Qi Zhao, Yingye Zheng

Institutions: University of Washington, Fred Hutch Cancer Center

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute

Awards: G7240532183, G9000033087, The Early Detection Research Network: Data Management and Coordinating Center, Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating Unit