Huber Cynthia, Benda Norbert, Friede Tim
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Federal Institute for Drugs and Medical Devices (BfArM) Research Department, Bonn, Germany.
Pharm Stat. 2019 Oct;18(5):600-626. doi: 10.1002/pst.1951. Epub 2019 Jul 3.
With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model-based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre-specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis.
随着基因组测序等技术的进步，预测性生物标志物已成为个性化医疗发展的有用工具。预测性生物标志物可用于选择最有可能从治疗中获益的患者亚组。在过去几年中，人们提出了许多亚组识别方法。尽管已有亚组识别方法的综述，但在模拟研究中对其性能进行系统比较的情况却很少见。为了进行亚组识别，人们提出了交互树（IT）、基于模型的递归划分、基于差异效应的亚组识别、同时阈值交互建模算法（STIMA）以及通过定向剥离进行自适应细化等方法。我们在一项模拟研究中采用结构化方法对这些方法进行了比较。为了确定后续试验的目标人群，需要从已识别的亚组中进行选择。因此，我们提出了一种亚组标准，该标准导致目标亚组由估计治疗差异不低于预先指定阈值的已识别亚组组成。在我们的模拟研究中，我们通过考虑二元分类的指标（如敏感性和特异性）来评估这些方法。在效应大或样本量巨大的情况下，大多数方法表现良好。然而，在药物开发中仅涉及来自单个试验数据的更现实情况下，似乎没有一种方法适合选择目标人群。使用亚组标准替代所提出的修剪程序，STIMA和IT在某些情况下可以提高其性能。通过肌萎缩侧索硬化症的应用实例说明了这些方法和亚组标准。
