In clinical practice, there is significant interest in integrating novel biomarkers with existing clinical data to construct interpretable and robust decision rules. Motivated by the need to improve decision-making for early disease detection, we propose a framework for developing an optimal biomarker-based clinical decision rule that is both clinically meaningful and practically feasible. Specifically, our procedure constructs a linear decision rule designed to achieve optimal performance among class of linear rules by maximizing the true positive rate while adhering to a pre-specified positive predictive value constraint. Additionally, our method can adaptively incorporate individual risk information from external source to enhance performance when such information is beneficial. We establish the asymptotic properties of our proposed estimator and compare to the standard approach used in practice through extensive simulation studies. Results indicate that our approach offers strong finite-sample performance. We also apply the proposed methods to develop biomarker-based screening rules for pancreatic ductal adenocarcinoma (PDAC) among new-onset diabetes (NOD) patients.

翻译：在临床实践中，整合新型生物标志物与现有临床数据以构建可解释且稳健的决策规则具有重要价值。基于提升早期疾病检测决策能力的需求，我们提出了一个开发基于生物标志物的最优临床决策规则的框架，该规则兼具临床意义与实际可行性。具体而言，我们的方法构建了一个线性决策规则，旨在通过最大化真阳性率，同时满足预先设定的阳性预测值约束，从而在线性规则类中实现最优性能。此外，当外部信息有益时，我们的方法能够自适应地整合来自外部源的个体风险信息以提升性能。我们建立了所提出估计量的渐近性质，并通过大量模拟研究将其与实践中使用的标准方法进行比较。结果表明，我们的方法具有优异的有限样本性能。我们还将所提出的方法应用于开发针对新发糖尿病（NOD）患者的胰腺导管腺癌（PDAC）生物标志物筛查规则。