将有前途的生物标志物融入癌症筛查实践，以便早期检测，变得越来越吸引人，因为当前的癌症筛查策略表现不尽如人意。匹配病例对照设计常常被采用于生物标志物发展研究，以评估标志物候选者的区别性能力，并意图消除混杂因素。匹配病例对照研究的数据常常通过条件逻辑回归进行分析，尽管假定标志物组合与疾病风险之间的对数连接并不总是成立。我们提出了一种无分布条件下的条件一致性辅助学习方法，用于鉴别病例和对照中标志物的最佳组合。我们特别关注具有临床和实际意义的特异性组合，以防止无病人群遭受不必要甚至有可能引起干预性的诊断程序，这是癌症人群筛查的重要优先事项。我们建立了衍生组合的渐近性质，并在模拟中验证了其尽可能地利于有限样本的表现。我们将所提出的方法应用于胡萝卜素和视黄醇疗效试验（CARET）的前列腺癌数据。© 2023 John Wiley＆Sons公司。

Incorporating promising biomarkers into cancer screening practices for early-detection is increasingly appealing because of the unsatisfactory performance of current cancer screening strategies. The matched case-control design is commonly adopted in biomarker development studies to evaluate the discriminative power of biomarker candidates, with an intention to eliminate confounding effects. Data from matched case-control studies have been routinely analyzed by the conditional logistic regression, although the assumed logit link between biomarker combinations and disease risk may not always hold. We propose a conditional concordance-assisted learning method, which is distribution-free, for identifying an optimal combination of biomarkers to discriminate cases and controls. We are particularly interested in combinations with a clinically and practically meaningful specificity to prevent disease-free subjects from unnecessary and possibly intrusive diagnostic procedures, which is a top priority for cancer population screening. We establish asymptotic properties for the derived combination and confirm its favorable finite sample performance in simulations. We apply the proposed method to the prostate cancer data from the carotene and retinol efficacy trial (CARET).© 2023 John Wiley & Sons Ltd.