Institute of Medical Biometry and Informatics, University Medical Center Ruprecht-Karls University Heidelberg, Heidelberg, Germany.
MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge, UK.
Stat Med. 2021 Jun 15;40(13):3196-3213. doi: 10.1002/sim.8953. Epub 2021 Mar 18.
Adaptive designs are playing an increasingly important role in the planning of clinical trials. While there exists various research on the optimal determination of a two-stage design, non-optimal versions still are frequently applied in clinical research. In this article, we strive to motivate the application of optimal adaptive designs and give guidance on how to determine them. It is demonstrated that optimizing a trial design with respect to particular objective criteria can have a substantial benefit over the application of conventional adaptive sample size recalculation rules. Furthermore, we show that in many practical situations, optimal group-sequential designs show an almost negligible performance loss compared to optimal adaptive designs. Finally, we illustrate how optimal designs can be tailored to specific operational requirements by customizing the underlying optimization problem.
自适应设计在临床试验的规划中扮演着越来越重要的角色。虽然对于两阶段设计的最优确定有各种各样的研究，但非最优版本仍经常应用于临床研究中。在本文中，我们努力推动最优自适应设计的应用，并提供如何确定它们的指导。结果表明，相对于传统的自适应样本量重估规则，针对特定目标标准优化试验设计可以带来实质性的好处。此外，我们还表明，在许多实际情况下，与最优自适应设计相比，最优分组序贯设计的性能损失几乎可以忽略不计。最后，我们通过定制底层优化问题来说明如何根据特定的操作要求定制最优设计。
