Lipkovich Ilya, Dmitrienko Alex, B Ralph
Quintiles, Inc., Durham, NC, U.S.A.
Mediana, Inc., Overland Park, KS, U.S.A.
Stat Med. 2017 Jan 15;36(1):136-196. doi: 10.1002/sim.7064. Epub 2016 Aug 3.
It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad-hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad-hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright © 2016 John Wiley & Sons, Ltd.
众所周知，临床试验中治疗效果的方向和大小通常会受到患者基线特征（通常称为生物标志物）的影响。治疗效果异质性的表征在个性化医疗领域中起着核心作用，并有助于量身定制疗法的开发。本教程重点关注数据驱动的亚组分析中出现的一类常见问题，即识别具有强预测特性的生物标志物以及具有诸如改善获益和/或安全性等理想特征的患者亚组。讨论了临床试验中生物标志物探索和亚组识别的临时方法的局限性，并将这些临时方法与基于机器学习和数据挖掘最新进展的探索性亚组分析的原则性方法进行了对比。介绍了评估预测性生物标志物和识别相关亚组的一般框架。本教程回顾了亚组发现中使用的一大类统计方法，包括全局结局建模方法、全局治疗效果建模方法、最优治疗方案和局部建模方法。使用基于具有二元和生存终点的临床试验的两个案例研究来说明常用的亚组识别方法。版权所有© 2016约翰威立父子有限公司。
