Do we need to adjust for interim analyses in a Bayesian adaptive trial design?
Do we need to adjust for interim analyses in a Bayesian adaptive trial design?
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DOI：
10.1186/s12874-020-01042-7
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发表时间：
2020-06-10
影响因子：
4
通讯作者：
Slade, Daniel
中科院分区：
文献类型：
--
作者：
Ryan, Elizabeth G.;Brock, Kristian;Slade, Daniel
背景：贝叶斯自适应方法越来越多地被用于设计临床试验，并提供了几个优于传统方法的优点。分析点的决策通常基于治疗效果的后验分布。然而，对于贝叶斯设计是否需要控制I型误差，存在一些混淆，因为这是一个频率主义概念。方法通过期中分析讨论贝叶斯试验中对多样性进行调整的正反两方面的争论。通过两个案例研究，我们说明了在贝叶斯临床试验中包括对类型I/II错误率的中期分析的效果，其中没有对多样性进行调整。我们提出了几种控制I型错误的方法，也提出了贝叶斯临床试验中决策的替代方法。结果在这两个案例研究中，我们都证明了在贝叶斯适应性设计中，通过纳入中期分析，允许为了疗效而提前停止，而不考虑多样性的调整，I类错误被夸大了。在某些情况下，纳入早期停药以提高疗效也增加了力量。增加中期分析的数量，只允许因无效而提前停止，减少了第一类错误，但也减少了功率。允许为了有效或徒劳而提前停止的临时分析的数量增加，通常会增加第一类错误和减少威力。结论目前，监管者要求频率自适应设计和贝叶斯自适应设计，特别是后期试验，都需要对I型误差的控制进行演示。为了证明在贝叶斯自适应设计中对I型错误的控制，通常需要调整停止边界，以便随着分析次数的增加而允许提前停止以获得效果。如果设计只允许无效的提前停止，则不需要调整停止边界来控制I类错误。如果人们转而使用严格的贝叶斯方法，这是目前在探索性试验的设计和分析中更被接受的方法，那么I类错误可以被忽略，而设计可以转而专注于临床相关值的治疗效果的后验概率。
Background Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept. Methods We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials. Results In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power. Conclusions Currently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values.