Zhang Zhiwei, Ma Shujie, Nie Lei, Soon Guoxing
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Int J Biostat. 2017 Mar 25;13(1):/j/ijb.2017.13.issue-1/ijb-2016-0064/ijb-2016-0064.xml. doi: 10.1515/ijb-2016-0064.
Motivated by an HIV example, we consider how to compare and combine treatment selection markers, which are essential to the notion of precision medicine. The current literature on precision medicine is focused on evaluating and optimizing treatment regimes, which can be obtained by dichotomizing treatment selection markers. In practice, treatment decisions are based not only on efficacy but also on safety, cost and individual preference, making it difficult to choose a single cutoff value for all patients in all settings. It is therefore desirable to have a statistical framework for comparing and combining treatment selection markers without dichotomization. We provide such a framework based on a quantitative concordance measure, which quantifies the extent to which higher marker values are predictive of larger treatment effects. For a given marker, the proposed concordance measure can be estimated from clinical trial data using a U-statistic, which can incorporate auxiliary covariate information through an augmentation term. For combining multiple markers, we propose to maximize the estimated concordance measure among a specified family of combination markers. A cross-validation procedure can be used to remove any re-substitution bias in assessing the quality of an optimized combination marker. The proposed methodology is applied to the HIV example and evaluated in simulation studies.
受一个艾滋病病毒（HIV）实例的启发，我们考虑如何比较和整合治疗选择标志物，这对精准医学的概念至关重要。当前关于精准医学的文献主要集中在评估和优化治疗方案上，这些方案可通过对治疗选择标志物进行二分法得到。在实践中，治疗决策不仅基于疗效，还基于安全性、成本和个人偏好，这使得在所有情况下为所有患者选择单一的临界值变得困难。因此，需要一个统计框架来比较和整合治疗选择标志物，而无需进行二分法。我们基于一种定量一致性度量提供了这样一个框架，该度量量化了较高的标志物值预示较大治疗效果的程度。对于给定的标志物，所提出的一致性度量可以使用U统计量从临床试验数据中估计出来，该统计量可以通过一个扩充项纳入辅助协变量信息。为了整合多个标志物，我们建议在指定的组合标志物族中最大化估计的一致性度量。可以使用交叉验证程序来消除评估优化后的组合标志物质量时的任何再代入偏差。所提出的方法应用于HIV实例，并在模拟研究中进行评估。
