EVALUATING A MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER

• 成果类型: Article
• 署名作者: Cheung, Ying Kuen; Kuhn, Louise
• 署名单位: Columbia University; Columbia University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2156
• 发表日期: 2026-06
• 页码: 1283-1300
• 关键词: Bayes classifier; HPV; multiplex assay; projection; recursive algorithm; sequential up-date; OPERATING CHARACTERISTIC CURVES; regression; FRAMEWORK

摘要

In a diagnostic test using multiplex assay, each individual biomarker is often expected to have monotonic association with the disease outcome, and, therefore, the underlying disease classification rule is partially ordered with respect to the biomarkers. Nonparametric estimation of the classification rule can be accomplished by projecting an unconstrained Bayes estimator onto the partial ordering subspace. However, computing the projection is challenging as it involves performing maximization over a constrained parameter space whose size grows exponentially with the sample size. We introduce a novel sequential update method for projection-based nonparametric estimation of the disease classification rule and propose new recursive algorithms to implement the method. The proposed algorithms yields the exact Bayes solution that maximizes the posterior gain with respect to a classification-type gain function. When compared to an existing algorithm that gives approximate Bayes solution, our algorithms accomplish the same tasks with much reduced elapsed time in simulation study. We apply the sequential update method to evaluate a human papillomavirus test for cervical cancer precursor lesions and derive diagnostic rule that improves accuracy on existing estimation methods including parametric logistic regression and monotone generalized additive models.