基于加权约登指数的生物标志物选择与组合
Biomarkers selection and combination based on the weighted Youden index
AI总结：
针对现有生物标志物诊断优化方法的局限，提出最大化加权约登指数的选择组合框架，搭配带SCAD惩罚的平滑估计与高效非凸优化算法，经模拟验证有效并用于皮炎诊断量表构建。
摘要：
在临床实践中，多种生物标志物被用于疾病诊断，但它们各自的准确性往往欠佳，仅有少数被证实与疾病直接相关。有效选择并组合生物标志物可显著提升诊断准确性。现有方法通常优化ROC曲线下面积（AUC）或约登指数（Youden index）等指标。然而，优化AUC无法得到最优截断值的估计，且约登指数假设灵敏度与特异度权重相等，这可能无法反映临床中两类指标权重不同的实际优先级需求，凸显了开发可灵活适配此类需求的方法的必要性。本文提出一种新型生物标志物选择与组合框架，旨在最大化加权版约登指数。我们引入了基于加权约登指数的平滑估计量，并提出采用SCAD惩罚项的惩罚版本以增强变量选择效果。为处理目标函数的非凸性与惩罚项的非光滑性，我们开发了一种高效算法，该算法同样适用于其他非凸优化问题。模拟研究验证了本方法的性能与效率，我们还将其应用于构建皮炎的诊断量表。
英文摘要：
In clinical practice, multiple biomarkers are used for disease diagnosis, but their individual accuracies are often suboptimal, with only a few proving directly relevant. Effectively selecting and combining biomarkers can significantly improve diagnostic accuracy. Existing methods often optimize metrics like the Area Under the ROC Curve (AUC) or the Youden index. However, optimizing AUC does not yield estimates for optimal cutoff values, and the Youden index assumes equal weighting of sensitivity and specificity, which may not reflect clinical priorities where these metrics are weighted differently. This highlights the need for methods that can flexibly accommodate such requirements. In this paper, we present a novel framework for selecting and combining biomarkers to maximize a weighted version of the Youden index. We introduce a smoothed estimator based on the weighted Youden index and propose a penalized version using the SCAD penalty to enhance variable selection. To handle the non-convexity of the objective function and the non-smoothness of the penalty, we develop an efficient algorithm, also applicable to other non-convex optimization problems. Simulation studies demonstrate the performance and efficiency of our method, and we apply it to construct a diagnostic scale for dermatitis.
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