Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
School of Medicine, Stanford University, Stanford, California, USA.
Stat Med. 2022 Nov 20;41(26):5242-5257. doi: 10.1002/sim.9565. Epub 2022 Aug 31.
Development of marker signatures to predict treatment benefits for a new therapeutic is an important scientific component in advancing the drug discovery and is an important first step toward the goal of precision medicine. In this article, we focus on developing an algorithm to search for optimal linear combination of markers that maximizes the area between two receiver operating characteristic curves of the new therapeutic and the control groups without assuming any parametric model. We further generalize the proposed algorithm for predictive signature development to maximize the difference of Harrel's C-index of the new therapeutic and the control groups when the outcome of interest is time-to-event. The performance of this proposed method is evaluated and compared to existing methods via simulations and real clinical trial data.
开发预测新型治疗方法疗效的标志物特征是推进药物发现的重要科学组成部分，也是精准医学目标的重要第一步。在本文中，我们专注于开发一种算法，在不假设任何参数模型的情况下，搜索新治疗方法和对照组之间的两个接收器工作特征曲线之间的最佳标记的线性组合。当感兴趣的结果是时间事件时，我们进一步将所提出的用于预测特征开发的算法推广为最大化新治疗方法和对照组的 Harrell C 指数之间的差异。通过模拟和真实临床试验数据，评估并比较了该方法的性能与现有方法。
