Li Qian H
Center for Drug Evaluation and Research, US FDA, Silve Spring, MD 20993, USA.
Biom J. 2009 Feb;51(1):137-45. doi: 10.1002/bimj.200710497.
Often a treatment is assessed by co-primary endpoints so that a comprehensive picture of the treatment effect can be obtained. Co-primary endpoints can be different medical assessments angled at different aspects of a disease, therefore, are used collectively to strengthen evidence for the treatment effect. It is common sense that if a treatment is ineffective, the chance to show that the treatment is effective in all co-primary endpoints should be small. Therefore, it may not be necessary to require all the co-primary endpoints to be statistically significant at the 1-sided 0.025 level to control the error rate of wrongly approving an ineffective treatment. Rather it is reasonable to allow certain variation for the p -values within a range close to 0.025. In this paper, statistical methods are developed to derive decision rules to evaluate co-primary endpoints collectively. The decision rules control the error rate of wrongly accepting an ineffective treatment at the level of 0.025 for a study and the error rate at a slightly higher level for a treatment that works for all the co-primary endpoints except perhaps one. The decision rules also control the error rates for individual endpoints. Potential applications in clinical trials are presented.
通常通过共同主要终点来评估一种治疗方法，以便能够全面了解治疗效果。共同主要终点可以是针对疾病不同方面的不同医学评估，因此，它们被共同用于加强治疗效果的证据。常识是，如果一种治疗方法无效，那么在所有共同主要终点上都显示该治疗方法有效的机会应该很小。因此，可能没有必要要求所有共同主要终点在单侧0.025水平上都具有统计学显著性，以控制错误批准无效治疗的错误率。相反，允许p值在接近0.025的范围内有一定变化是合理的。在本文中，开发了统计方法来推导决策规则，以共同评估共同主要终点。这些决策规则将一项研究中错误接受无效治疗的错误率控制在0.025水平，对于除了可能一个共同主要终点外对所有共同主要终点都有效的治疗方法，将错误率控制在略高的水平。这些决策规则还控制各个终点的错误率。文中介绍了在临床试验中的潜在应用。
