Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago.
NEJM Evid. 2023 Nov;2(11):EVIDe2300250. doi: 10.1056/EVIDe2300250. Epub 2023 Oct 24.
Increasingly, investigators are choosing to use Bayesian methods for the analysis of clinical trial data. Unlike classical statistical methods that treat model parameter values (such as treatment effects) as fixed, Bayesian methods view parameters as following a probability distribution. As we have written previously, by analyzing clinical trial data using Bayesian methods one can obtain quantities that may be of interest to clinicians, providers, and patients, such as the probability that a treatment effect is more or less than 0, that is, the probability that a treatment is effective.
越来越多的研究人员选择使用贝叶斯方法来分析临床试验数据。与将模型参数值(如治疗效果)视为固定值的经典统计方法不同,贝叶斯方法将参数视为遵循概率分布。正如我们之前所写的,通过使用贝叶斯方法分析临床试验数据,人们可以获得可能对临床医生、提供者和患者感兴趣的数量,例如治疗效果大于或小于 0 的概率,即治疗有效的概率。