Paul Erina, Tiwari Ram C, Chowdhury Shrabanti, Ghosh Samiran
Center of Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA.
Division of Biostatistics, Center for Devices and Radiological Health, Office Surveillance and Biometrics, FDA, Silver Spring, MD, USA.
J Appl Stat. 2021 Nov 11;50(4):848-870. doi: 10.1080/02664763.2021.1998391. eCollection 2023.
Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.
在许多传统治疗领域寻找改进干预措施的必要性至关重要。这有可能降低医疗成本,并改善许多患者的不良预后。通常,临床疗效是衡量治疗任何有益效果的主要评估标准。尽管如此,在某些情况下,其他几个因素(如副作用、成本负担、较弱的衰弱程度、较低的强度等)可能会使一些疗效稍差的治疗方案对一部分患者更有利。这通常会导致非劣效性(NI)检验。由于伦理原因,NI试验可能包含也可能不包含安慰剂组。然而,当包含安慰剂组时,由此产生的三臂试验更为谨慎,因为与无安慰剂的双臂试验相比,它所需的假设不那么严格。在本文中,当感兴趣的函数为风险差时,我们考虑在具有二元结果的三臂试验中检验NI的频率学派和贝叶斯方法。首先提出一种改进的频率学派方法,随后是贝叶斯对应方法。贝叶斯方法在许多活性对照试验(包括NI试验)中具有天然优势,因为它可以无缝整合大量先验信息。此外,我们讨论样本量计算,并在两种范式之间建立有趣的联系。