Two-step Error-controlling Classifiers

Music City Center 

Accurate classifiers that utilize novel biomarkers and readily available predictors significantly enhance decision-making in various clinical scenarios, such as in assessing the need for biopsies in cancer diagnosis. When classification performance is limited, a decision framework can be applied to effectively rule in or rule out diagnoses while incorporating a neutral zone for indeterminate classifications. Building on this framework, we propose a new family of two-step classifiers that selectively employ costly biomarker testing for a targeted subset of individuals undergoing multiple evaluations. This optimal solution expands upon the Neyman-Pearson Lemma, highlighting a vital trade-off between the costs of expensive biomarker measurements and the improvement of 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.

biomarker

classification

sequential testing