One central task in precision medicine is to establish individualized treatment rules (ITRs) for patients with heterogeneous responses to different therapies. Motivated from a randomized clinical trial for Type 2 diabetic patients on a comparison of two drugs, that is, pioglitazone and gliclazide, we consider a problem: utilizing promising candidate biomarkers to improve an existing ITR. This calls for a biomarker evaluation procedure that enables to gauge added values of individual biomarkers. We propose an assessment analytic, termed as net benefit index (NBI), that quantifies a contrast between the resulting gain and loss of treatment benefits when a biomarker enters ITR to reallocate patients in treatments. We optimize reallocation schemes via outcome weighted learning (OWL), from which the optimal treatment group labels are generated by weighted support vector machine (SVM). To account for sampling uncertainty in assessing a biomarker, we propose an NBI-based test for a significant improvement over the existing ITR, where the empirical null distribution is constructed via the method of stratified permutation by treatment arms. Applying NBI to the motivating diabetes trial, we found that baseline fasting insulin is an important biomarker that leads to an improvement over an existing ITR based only on patient's baseline fasting plasma glucose (FPG), age, and body mass index (BMI) to reduce FPG over a period of 52 weeks.

中文翻译：

---

净收益指数：评估生物标志物对个体化治疗规则的影响

精准医疗的一项核心任务是为对不同疗法有不同反应的患者建立个体化治疗规则 (ITR)。受 2 型糖尿病患者随机临床试验的启发，我们比较了两种药物，即吡格列酮和格列齐特，我们考虑了一个问题：利用有希望的候选生物标志物来改善现有的 ITR。这需要一种能够衡量单个生物标志物附加值的生物标志物评估程序。我们提出了一种评估分析，称为净收益指数（NBI），它量化了当生物标志物进入 ITR 以在治疗中重新分配患者时所产生的治疗益处的收益和损失之间的对比。我们通过结果加权学习 (OWL) 优化重新分配方案，通过加权支持向量机 (SVM) 从中生成最佳治疗组标签。为了解释评估生物标志物时的抽样不确定性，我们提出了一种基于 NBI 的测试，以显着改进现有的 ITR，其中经验零分布是通过治疗组的分层排列方法构建的。将 NBI 应用于激励糖尿病试验，我们发现基线空腹胰岛素是一个重要的生物标志物，它可以改善现有的 ITR，仅基于患者的基线空腹血糖 (FPG)、年龄、