Department of Biostatistics, University of North Carolina, Chapel Hill, 27599, North Carolina, USA.
Stat Med. 2018 Nov 20;37(26):3814-3831. doi: 10.1002/sim.7846. Epub 2018 Jun 25.
In this paper, we develop a general Bayesian clinical trial design methodology, tailored for time-to-event trials with a cured fraction in scenarios where a previously completed clinical trial is available to inform the design and analysis of the new trial. Our methodology provides a conceptually appealing and computationally feasible framework that allows one to construct a fixed, maximally informative prior a priori while simultaneously identifying the minimum sample size required for the new trial so that the design has high power and reasonable type I error control from a Bayesian perspective. This strategy is particularly well suited for scenarios where adaptive borrowing approaches are not practical due to the nature of the trial, complexity of the model, or the source of the prior information. Control of a Bayesian type I error rate offers a sensible balance between wanting to use high-quality information in the design and analysis of future trials while still controlling type I errors in an equitable way. Moreover, sample size determination based on our Bayesian view of power can lead to a more adequately sized trial by virtue of taking into account all the uncertainty in the treatment effect. We demonstrate our methodology by designing a cancer clinical trial in high-risk melanoma.
在本文中，我们开发了一种通用的贝叶斯临床试验设计方法，适用于具有治愈部分的生存时间试验，并且在有先前完成的临床试验可用于为新试验的设计和分析提供信息的情况下。我们的方法提供了一个概念上吸引人且计算上可行的框架，允许在构建固定的、信息量最大的先验的同时，确定新试验所需的最小样本量，以便从贝叶斯的角度来看，设计具有高功效和合理的 I 类错误控制。这种策略特别适用于由于试验的性质、模型的复杂性或先验信息的来源，自适应借用方法不可行的情况。控制贝叶斯 I 类错误率在设计和分析未来试验时希望使用高质量信息与以公平的方式控制 I 类错误之间提供了合理的平衡。此外，基于我们对功效的贝叶斯观点的样本量确定可以通过考虑治疗效果的所有不确定性来导致更充分的试验。我们通过设计高风险黑色素瘤的癌症临床试验来演示我们的方法。
