Family Medicine & Public Health Sciences and Center of Molecular Medicine and Genetics, Wayne State University.
Center of Molecular Medicine and Genetics, Wayne State University.
Biostatistics. 2022 Jan 13;23(1):136-156. doi: 10.1093/biostatistics/kxaa014.
With the availability of limited resources, innovation for improved statistical method for the design and analysis of randomized controlled trials (RCTs) is of paramount importance for newer and better treatment discovery for any therapeutic area. Although clinical efficacy is almost always the primary evaluating criteria to measure any beneficial effect of a treatment, there are several important other factors (e.g., side effects, cost burden, less debilitating, less intensive, etc.), which can permit some less efficacious treatment options favorable to a subgroup of patients. This leads to non-inferiority (NI) testing. The objective of NI trial is to show that an experimental treatment is not worse than an active reference treatment by more than a pre-specified margin. Traditional NI trials do not include a placebo arm for ethical reason; however, this necessitates stringent and often unverifiable assumptions. On the other hand, three-arm NI trials consisting of placebo, reference, and experimental treatment, can simultaneously test the superiority of the reference over placebo and NI of experimental treatment over the reference. In this article, we proposed both novel Frequentist and Bayesian procedures for testing NI in the three-arm trial with Poisson distributed count outcome. RCTs with count data as the primary outcome are quite common in various disease areas such as lesion count in cancer trials, relapses in multiple sclerosis, dermatology, neurology, cardiovascular research, adverse event count, etc. We first propose an improved Frequentist approach, which is then followed by it's Bayesian version. Bayesian methods have natural advantage in any active-control trials, including NI trial when substantial historical information is available for placebo and established reference treatment. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.
由于资源有限，创新改进的统计方法对于随机对照试验（RCT）的设计和分析至关重要，这对于任何治疗领域的新疗法和更好的疗法发现都具有重要意义。虽然临床疗效几乎总是衡量治疗效果的主要评估标准，但还有其他几个重要因素（例如副作用、成本负担、较少致残、较少强化等），这些因素可以使某些疗效较低的治疗选择对某些患者亚组有利。这就需要进行非劣效性（NI）检验。NI 试验的目的是证明，实验性治疗与阳性对照治疗相比，不超过预先指定的边界，效果不劣于阳性对照治疗。传统的 NI 试验由于伦理原因不包括安慰剂组；然而，这需要严格且常常难以验证的假设。另一方面，由安慰剂、阳性对照和实验性治疗组成的三臂 NI 试验可以同时检验阳性对照相对于安慰剂的优越性和实验性治疗相对于阳性对照的 NI。在本文中，我们提出了用于检验三臂试验中具有泊松分布计数结局的 NI 的新的 Frequentist 和贝叶斯程序。以计数数据作为主要结局的 RCT 在各种疾病领域都很常见，如癌症试验中的病变计数、多发性硬化症、皮肤病学、神经病学、心血管研究、不良事件计数等。我们首先提出了一种改进的 Frequentist 方法，然后是它的贝叶斯版本。贝叶斯方法在任何阳性对照试验中都具有天然优势，包括当有大量关于安慰剂和已建立的阳性对照治疗的历史信息时的 NI 试验。此外，我们还讨论了样本量计算，并在两种方法之间建立了有趣的联系。
