Hee Siew Wan, Parsons Nicholas, Stallard Nigel
Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Biom J. 2018 Mar;60(2):232-245. doi: 10.1002/bimj.201600202. Epub 2017 Jul 26.
The motivation for the work in this article is the setting in which a number of treatments are available for evaluation in phase II clinical trials and where it may be infeasible to try them concurrently because the intended population is small. This paper introduces an extension of previous work on decision-theoretic designs for a series of phase II trials. The program encompasses a series of sequential phase II trials with interim decision making and a single two-arm phase III trial. The design is based on a hybrid approach where the final analysis of the phase III data is based on a classical frequentist hypothesis test, whereas the trials are designed using a Bayesian decision-theoretic approach in which the unknown treatment effect is assumed to follow a known prior distribution. In addition, as treatments are intended for the same population it is not unrealistic to consider treatment effects to be correlated. Thus, the prior distribution will reflect this. Data from a randomized trial of severe arthritis of the hip are used to test the application of the design. We show that the design on average requires fewer patients in phase II than when the correlation is ignored. Correspondingly, the time required to recommend an efficacious treatment for phase III is quicker.
本文工作的动机在于，在II期临床试验中有多种治疗方法可供评估，但由于目标人群规模较小，同时尝试这些治疗方法可能不可行。本文介绍了先前关于一系列II期试验的决策理论设计工作的扩展。该方案包括一系列具有中期决策的序贯II期试验和一项单一的双臂III期试验。该设计基于一种混合方法，其中III期数据的最终分析基于经典的频率主义假设检验，而试验则使用贝叶斯决策理论方法进行设计，其中假设未知治疗效果服从已知的先验分布。此外，由于这些治疗方法针对的是同一人群，考虑治疗效果相关并非不切实际。因此，先验分布将反映这一点。来自一项严重髋关节炎随机试验的数据用于检验该设计的应用。我们表明，与忽略相关性时相比，该设计在II期平均所需的患者数量更少。相应地，为III期推荐有效治疗所需的时间更快。
