School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Cancer Research UK - Cambridge Institute, University of Cambridge, Cambridge, UK.
Respirology. 2022 Oct;27(10):834-843. doi: 10.1111/resp.14337. Epub 2022 Aug 2.
The use of Bayesian adaptive designs for clinical trials has increased in recent years, particularly during the COVID-19 pandemic. Bayesian adaptive designs offer a flexible and efficient framework for conducting clinical trials and may provide results that are more useful and natural to interpret for clinicians, compared to traditional approaches. In this review, we provide an introduction to Bayesian adaptive designs and discuss its use in recent clinical trials conducted in respiratory medicine. We illustrate this approach by constructing a Bayesian adaptive design for a multi-arm trial that compares two non-invasive ventilation treatments to standard oxygen therapy for patients with acute cardiogenic pulmonary oedema. We highlight the benefits and some of the challenges involved in designing and implementing Bayesian adaptive trials.
近年来，贝叶斯自适应设计在临床试验中的应用有所增加，尤其是在 COVID-19 大流行期间。贝叶斯自适应设计为进行临床试验提供了一个灵活高效的框架，与传统方法相比，它可能为临床医生提供更有用和更自然的解释结果。在这篇综述中，我们介绍了贝叶斯自适应设计，并讨论了它在呼吸医学领域最近进行的临床试验中的应用。我们通过构建一个比较两种无创通气治疗与急性心源性肺水肿患者标准氧疗的多臂试验的贝叶斯自适应设计来说明这种方法。我们强调了设计和实施贝叶斯自适应试验所涉及的一些好处和挑战。
