Petroni Gina R, Wages Nolan A, Paux Gautier, Dubois Frédéric
Division of Translational Research and Applied Statistics, Department of Public Health Sciences, The University of Virginia, Charlottesville, VA, 22908, U.S.A.
Oncology Clinical Biostatistics, Institut de Recherches Internationales Servier (IRIS), Suresnes Cedex, 92284, France.
Stat Med. 2017 Jan 30;36(2):215-224. doi: 10.1002/sim.6910. Epub 2016 Feb 29.
There has been constant development of novel statistical methods in the design of early-phase clinical trials since the introduction of model-based designs, yet the traditional or modified 3+3 algorithmic design remains the most widely used approach in dose-finding studies. Research has shown the limitations of this traditional design compared with more innovative approaches yet the use of these model-based designs remains infrequent. This can be attributed to several causes including a poor understanding from clinicians and reviewers into how the designs work, and how best to evaluate the appropriateness of a proposed design. These barriers are likely to be enhanced in the coming years as the recent paradigm of drug development involves a shift to more complex dose-finding problems. This article reviews relevant information that should be included in clinical trial protocols to aid in the acceptance and approval of novel methods. We provide practical guidance for implementing these efficient designs with the aim of augmenting a broader transition from algorithmic to adaptive model-guided designs. In addition we highlight issues to consider in the actual implementation of a trial once approval is obtained. Copyright © 2016 John Wiley & Sons, Ltd.
自从引入基于模型的设计以来,早期临床试验设计中的新型统计方法不断发展,但传统的或改良的3+3算法设计仍然是剂量探索研究中使用最广泛的方法。研究表明,与更具创新性的方法相比,这种传统设计存在局限性,但这些基于模型的设计的使用仍然很少。这可归因于几个原因,包括临床医生和审评人员对这些设计的工作原理以及如何最好地评估拟议设计的适宜性了解不足。随着最近药物开发范式转向更复杂的剂量探索问题,这些障碍在未来几年可能会加剧。本文回顾了临床试验方案中应包含的相关信息,以帮助新型方法的接受和批准。我们提供了实施这些高效设计的实用指南,目的是促进从算法设计到适应性模型引导设计的更广泛转变。此外,我们强调了获得批准后在试验实际实施中需要考虑的问题。版权所有© 2016约翰威立父子有限公司。