Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Biom J. 2020 Sep;62(5):1264-1283. doi: 10.1002/bimj.201900020. Epub 2020 Mar 2.
Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so-called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.
自适应无缝设计将确证性测试（III 期试验的一个领域）与治疗或亚组选择等特征结合起来，这些特征通常与 II 期试验相关联。它们有望提高新药开发计划的效率，例如在样本量和/或开发时间方面。众所周知，自适应设计在逻辑上更复杂，需要更多的前期规划，通常以广泛的模拟研究的形式进行，而不是传统方法。在这里，我们使用相同的符号提出了一种自适应治疗和亚组选择的框架，将关于治疗选择的文献和关于亚组选择的文献联系起来。此外，我们引入了一个灵活高效的模拟模型，为这两种设计服务。由于主要终点通常需要很长时间才能观察到，因此中期分析通常会根据早期结果进行信息更新。因此，所有呈现的方法都可以接受基于主要终点或早期结果的中期分析。先前为模拟具有治疗选择的设计而开发的 R 包 asd 已扩展到包括亚组选择（所谓的自适应富集设计）。在这里，我们描述了 R 包 asd 的功能，并使用它呈现了一些由慢性阻塞性肺疾病和肿瘤学临床试验激发的示例。这些示例既说明了 R 包的各种功能，也提供了对自适应无缝研究的操作特征的深入了解。
