Liu Qing, Li Yulan, Odem-Davis Katherine
a AbacusCloud, LLC , Long Valley , New Jersey , USA.
J Biopharm Stat. 2015;25(1):206-25. doi: 10.1080/10543406.2014.923738.
The regulatory guidelines on noninferiority (NI) trials emphasize constancy not only in the treatment effect over time but also in the trial design, clinical practice, and quality of the trial conduct and execution. In practice, the constancy assumption is generally impossible to justify; often, there are clear reasons to expect a loss of efficacy over time. There are also concerns about the inherent and publication bias in the historical data, and various sources of selection bias in the NI trial design. Thus, a conservative NI margin is often considered. However, different NI margin approaches are largely evaluated under the assumption of constancy and absence of bias, and therefore, controversies arise and are unresolved on the necessary degree of conservativeness. We develop a framework to quantify the robustness of any NI margin approach against inherent and publication bias in historical data, selection bias in trial design, and nonconstancy in reference effects. We introduce a consistency principle to address variability in the historical data. We control across-trial conditional error rates given a final NI trial design over a design specific robust range for reference effects. Following a conditionality principle, we provide a theoretical justification of the framework and the conditions for controlling across-trial unconditional type 1 error rates. We raise the issue of inherent bias in historical data with an illustrative example.
非劣效性(NI)试验的监管指南强调不仅在治疗效果随时间的稳定性,还包括试验设计、临床实践以及试验实施和执行的质量方面的稳定性。在实际操作中,稳定性假设通常无法得到合理证明;通常,有明显的理由预期随着时间推移疗效会下降。人们还担心历史数据中存在的固有偏倚和发表偏倚,以及NI试验设计中的各种选择偏倚来源。因此,通常会考虑采用保守的NI界值。然而,不同的NI界值方法在很大程度上是在稳定性和无偏倚的假设下进行评估的,因此,关于必要的保守程度出现了争议且尚未解决。我们开发了一个框架,用于量化任何NI界值方法针对历史数据中的固有偏倚和发表偏倚、试验设计中的选择偏倚以及对照效应的不稳定性的稳健性。我们引入了一个一致性原则来解决历史数据中的变异性。我们在给定最终NI试验设计的情况下,针对对照效应的特定设计稳健范围控制跨试验条件错误率。遵循条件性原则,我们为该框架以及控制跨试验无条件一类错误率的条件提供了理论依据。我们通过一个示例提出了历史数据中固有偏倚的问题。