PDF(Pubmed)
Abstract：
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).
摘要：
由于当前癌症筛查策略的表现不令人满意，将有希望的生物标志物纳入癌症筛查实践以进行早期检测越来越有吸引力。匹配的病例对照设计通常在生物标志物开发研究中采用，以评估生物标志物候选的辨别能力。目的是消除混杂效应。来自匹配病例对照研究的数据已经通过条件逻辑回归进行了常规分析，尽管假定的生物标志物组合与疾病风险之间的logit联系可能并不总是成立.我们提出了一种条件一致性辅助学习方法，这是免费分发的，用于确定生物标志物的最佳组合以区分病例和对照。我们特别感兴趣的是结合临床和实践上有意义的特异性，以防止无病受试者不必要的和可能侵入性的诊断程序，这是癌症人群筛查的重中之重。我们为导出的组合建立渐近性质，并在模拟中证实其有利的有限样本性能。我们将提出的方法应用于来自胡萝卜素和视黄醇功效试验（CARET）的前列腺癌数据。
