Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, USPC, Université de Paris, F-75006 Paris, France.
F-CRIN PARTNERS Platform, AP-HP, Université de Paris, F-75010 Paris, France.
Int J Environ Res Public Health. 2021 Jan 24;18(3):1022. doi: 10.3390/ijerph18031022.
The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.
本文的目的是向读者介绍贝叶斯方法，这些方法在罕见病领域中似乎是最重要的。一种疾病被定义为罕见病，取决于受影响患者在考虑人群中的流行程度，例如在美国约为每 1500 人中有 1 人患病，在日本约为每 2500 人中有 1 人患病，在欧洲则少于每 2000 人中有 1 人患病。有 6000 至 8000 种罕见病，药物开发的主要问题与在小人群中从临床试验中获得可靠证据的挑战有关。更好地利用所有可用信息可以帮助开发过程，贝叶斯统计可以在设计阶段、试验进行期间和分析阶段提供一个坚实的框架。本文的重点是提供对罕见病中 II 期或 III 期试验中样本量计算或重新评估、反应适应性随机化以及荟萃分析的贝叶斯方法的综述。还讨论了先验分布选择、计算负担和传播方面的挑战。
