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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\u2010step classifiers that selectively use costly biomarker testing for a targeted subset of individuals undergoing multiple evaluations. The optimal solution expands upon the Neyman\u2010Pearson Lemma, highlighting a vital trade\u2010off 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.<\/jats:p>","DOI":"10.1002\/sim.70498","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:21:50Z","timestamp":1775114510000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two\u2010Step Error\u2010Controlling Classifiers With Application to Cost\u2010Effective Disease Diagnosis"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6548-4707","authenticated-orcid":false,"given":"Kehao","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Biostatistics University of Washington  Seattle Washington USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwun","family":"Chuen Gary Chan","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Washington  Seattle Washington 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