Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials.
Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials.
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DOI：
10.1177/1740774517729167
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Carlin BP
中科院分区：
文献类型：
--
作者：
Schnell PM;Müller P;Tang Q;Carlin BP
健康科学最近的一个焦点是个性化医疗的发展，其中包括确定特定治疗有效的人群。由于数据有限，确定真正的受益人群是一项具有挑战性的任务。为了解决这个困难，可信子群方法为真正的受益子群提供了一对边界子群，构造为使得一个包含在受益子群中，而另一个包含具有高概率的受益子群。然而，该方法迄今为止仅针对参数线性模型开发。在本文中，我们开发了所需的细节，以遵循可信的子群的方法，在更现实的设置，考虑非线性和半参数回归模型，支持监管科学的条件功率模拟。我们还提出了一种改进的多重测试方法，使用逐步下降的过程。我们通过模拟来评估我们的方法，并将其应用于AbbVie进行的四项阿尔茨海默病治疗试验的数据。半参数建模产生了可信的亚组，这些亚组对违反线性治疗效应假设更稳健，并且仔细选择感兴趣的人群以及逐步下降的多重检验程序导致受益患者类型的检出率更高。这种方法使我们能够确定在阿尔茨海默病试验中受益于治疗的患者类型。由于缺乏多重性控制和不切实际的限制性假设，在临床试验中识别受益患者亚组的尝试经常受到怀疑。我们提出的方法融合了两种技术，可信子群和半参数回归，避免了这些问题，使有益的子群识别实用和可靠。
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. In this paper 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. 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. 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.
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影响因子：
2
作者：
Foster, Jared C.;Taylor, Jeremy M. G.;Ruberg, Stephen J.
通讯作者：
Ruberg, Stephen J.
影响因子：
1.9
作者：
Schnell PM;Tang Q;Offen WW;Carlin BP
通讯作者：
Carlin BP
影响因子：
2.7
作者：
MARCUS, R;PERITZ, E;GABRIEL, KR
通讯作者：
GABRIEL, KR
影响因子：
4.4
作者：
Müller P;Mitra R
通讯作者：
Mitra R
影响因子：
1.9
作者：
WRIGHT, SP
通讯作者：
WRIGHT, SP