Diamond G A, Forrester J S
Ann Intern Med. 1983 Mar;98(3):385-94. doi: 10.7326/0003-4819-98-3-385.
Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions.
传统的临床试验解释在很大程度上依赖于经典的p值。然而，p值仅代表假阳性率，并不能说明在研究者观察到的情况下其假设正确的概率。这种更具相关性的后验概率可以通过将贝叶斯定理扩展到统计检验分析来量化，其方式类似于已广泛用于诊断测试的方式。根据贝叶斯定理对几项已发表的临床试验进行重新分析，显示出经典统计分析的几个重要局限性。当所讨论的假设本身不太可能为真、基线事件发生率较低或观察到的差异较小时，经典分析最具误导性。在这种情况下，即使研究规模很大，仅基于p值进行解释时，假阳性和假阴性结论也经常出现。如果采用经过修订的临床试验分析和报告政策，利用适用的贝叶斯惯例克服经典统计理论的已知局限性，这些错误可以降至最低。
