Schnell Patrick M, Müller Peter, Tang Qi, Carlin Bradley P
1 Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
2 Department of Mathematics, The University of Texas at Austin, Austin, TX, USA.
Clin Trials. 2018 Feb;15(1):75-86. doi: 10.1177/1740774517729167. Epub 2017 Oct 16.
Background A recent focus in the health sciences has been the development of personalized medicine, which includes determining the population for which a given treatment is effective. Due to limited data, identifying the true benefiting population is a challenging task. To tackle this difficulty, the credible subgroups approach provides a pair of bounding subgroups for the true benefiting subgroup, constructed so that one is contained by the benefiting subgroup while the other contains the benefiting subgroup with high probability. However, the method has so far only been developed for parametric linear models. Methods In this article, we develop the details required to follow the credible subgroups approach in more realistic settings by considering nonlinear and semiparametric regression models, supported for regulatory science by conditional power simulations. We also present an improved multiple testing approach using a step-down procedure. We evaluate our approach via simulations and apply it to data from four trials of Alzheimer's disease treatments carried out by AbbVie. Results Semiparametric modeling yields credible subgroups that are more robust to violations of linear treatment effect assumptions, and careful choice of the population of interest as well as the step-down multiple testing procedure result in a higher rate of detection of benefiting types of patients. The approach allows us to identify types of patients that benefit from treatment in the Alzheimer's disease trials. Conclusion Attempts to identify benefiting subgroups of patients in clinical trials are often met with skepticism due to a lack of multiplicity control and unrealistically restrictive assumptions. Our proposed approach merges two techniques, credible subgroups, and semiparametric regression, which avoids these problems and makes benefiting subgroup identification practical and reliable.
背景 健康科学领域最近的一个重点是个性化医疗的发展，其中包括确定某种特定治疗方法对哪些人群有效。由于数据有限，确定真正受益的人群是一项具有挑战性的任务。为了解决这一难题，可信亚组方法为真正的受益亚组提供了一对边界亚组，其构建方式是一个亚组包含在受益亚组中，而另一个亚组很可能包含受益亚组。然而，到目前为止，该方法仅针对参数线性模型进行了开发。
方法 在本文中，我们通过考虑非线性和半参数回归模型，在更现实的环境中详细阐述了遵循可信亚组方法所需的内容，并通过条件功效模拟为监管科学提供支持。我们还提出了一种使用逐步递减程序的改进多重检验方法。我们通过模拟评估我们的方法，并将其应用于艾伯维公司进行的四项阿尔茨海默病治疗试验的数据。
结果 半参数建模产生的可信亚组对于违反线性治疗效果假设的情况更具稳健性，并且仔细选择感兴趣的人群以及逐步递减多重检验程序会导致更高的受益患者类型检测率。该方法使我们能够在阿尔茨海默病试验中识别出从治疗中受益的患者类型。
结论 由于缺乏多重性控制和不切实际的严格假设，在临床试验中识别受益患者亚组的尝试往往受到质疑。我们提出的方法融合了两种技术，即可信亚组和半参数回归，避免了这些问题，并使受益亚组的识别切实可行且可靠。
