Lewis R J, Wears R L
UCLA School of Medicine, Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance.
Ann Emerg Med. 1993 Aug;22(8):1328-36. doi: 10.1016/s0196-0644(05)80119-2.
Although most clinical trials comparing therapies are analyzed using classical hypothesis testing and P values, such methods do not yield the information most useful to the clinician, that is, the probability that one treatment is more efficacious than another. Bayesian inference can yield this probability but only if we quantify our prior beliefs about the possible efficacies of the treatments studied. This article gives a brief introduction to Bayesian methods and contrasts them with classical hypothesis testing. It shows that the quantification of prior beliefs is a common and necessary part of the interpretation of clinical information, whether from a laboratory test or published clinical trial. Advantages of Bayesian analysis over classical analysis of clinical trials include the ability to incorporate prior information regarding treatment efficacies into the analysis; the ability to make multiple unscheduled inspections of accumulating data without increasing the error rate of the study; and the ability to calculate the probability that one treatment is more effective than another. Because it is likely that Bayesian methods will be used more often in the analysis of future clinical trials, investigators and readers should be aware of the two schools of statistical thought and the strengths and weaknesses of each.
尽管大多数比较疗法的临床试验是使用经典假设检验和P值进行分析的，但这些方法并不能产生对临床医生最有用的信息，即一种治疗比另一种治疗更有效的概率。贝叶斯推断可以得出这个概率，但前提是我们要量化对所研究治疗方法可能疗效的先验信念。本文简要介绍了贝叶斯方法，并将其与经典假设检验进行了对比。结果表明，无论是来自实验室检测还是已发表的临床试验，先验信念的量化都是临床信息解读中常见且必要的一部分。与临床试验的经典分析相比，贝叶斯分析的优势包括能够将关于治疗疗效的先验信息纳入分析；能够对累积数据进行多次不定期检查而不增加研究的错误率；以及能够计算一种治疗比另一种治疗更有效的概率。由于贝叶斯方法很可能会在未来的临床试验分析中更频繁地使用，研究人员和读者应该了解这两种统计思想流派以及各自的优缺点。
