Nie Lei, Soon Guoxing Greg, Qi Karen, Chen Yong, Chu Haitao
U.S. Food and Drug Administration (US FDA), Division of Biometrics IV, Office of Biometrics/CDER/OTS/FDA, Silver Spring, Maryland 20993, USA.
J Biopharm Stat. 2013;23(5):1042-53. doi: 10.1080/10543406.2013.813523.
The traditional fixed margin approach to evaluating an experimental treatment through an active-controlled noninferiority trial is simple and straightforward. However, its utility relies heavily on the constancy assumption of the experimental data. The recently developed covariate-adjustment method permits more flexibility and improved discriminatory capacity compared to the fixed margin approach. However, one major limitation of this covariate-adjustment methodology is its adherence on the patient-level data, which may not be accessible to investigators in practice. In this article, under some assumptions, we examine the feasibility of a partial covariate-adjustment approach based on data typically available from journal publications or other public data when the patient-level data are unavailable. We illustrate the usefulness of this approach through two real examples. We also provide design considerations on the efficiency of the partial covariate-adjustment approach.
通过活性对照非劣效性试验评估实验性治疗的传统固定界值方法简单明了。然而,其效用在很大程度上依赖于实验数据的恒定性假设。与固定界值方法相比,最近开发的协变量调整方法具有更大的灵活性和更强的鉴别能力。然而,这种协变量调整方法的一个主要局限性在于它依赖于患者层面的数据,而在实际中研究人员可能无法获取这些数据。在本文中,在一些假设条件下,当无法获得患者层面的数据时,我们研究了一种基于期刊出版物或其他公共数据中通常可获得的数据的部分协变量调整方法的可行性。我们通过两个实际例子说明了这种方法的有用性。我们还提供了关于部分协变量调整方法效率的设计考量。