1 Statistics - Global Product Development, Pfizer Inc., New York, NY, USA.
2 Department of Biostatistics, School of Public Health & Health Professions, SUNY Buffalo, NY, USA.
Stat Methods Med Res. 2018 Dec;27(12):3658-3678. doi: 10.1177/0962280217710570. Epub 2017 Jun 20.
Subgroup identification with differential treatment effects serves as an important step towards precision medicine, as it provides evidence regarding how individuals with specific characteristics respond to a given treatment. This knowledge not only supports the tailoring of treatment strategies but also prompts the development of new treatments. This manuscript provides a brief overview of the issues associated with the methodologies aimed at identifying subgroups with differential treatment effects, and studies in depth the operational characteristics of five data-driven methods that have appeared recently in the literature. The performance of the methods under study to identify correctly the covariates affecting treatment effects is evaluated via simulation and under various conditions. Two clinical trial data sets are also used to illustrate the application of these methods. Discussion and recommendations pertaining to the use of these methods are provided, with emphasis on the relative performance of the methods under the conditions studied.
亚组识别与差异化治疗效果是精准医学的重要步骤，因为它为具有特定特征的个体对特定治疗的反应方式提供了证据。这些知识不仅支持治疗策略的定制，也促使新的治疗方法的开发。本文简要概述了与旨在识别差异化治疗效果亚组的方法相关的问题，并深入研究了最近在文献中出现的五种数据驱动方法的操作特点。通过模拟和在各种条件下，评估了所研究方法识别影响治疗效果的协变量的性能。还使用了两个临床试验数据集来说明这些方法的应用。讨论并建议了使用这些方法的相关问题，重点关注在研究条件下方法的相对性能。
