Jaki Thomas, Parry Alice
Department of Mathematics Statistics, Lancaster University, Lancaster, UK.
Pharm Stat. 2016 Jul;15(4):362-7. doi: 10.1002/pst.1751. Epub 2016 Apr 20.
Multiplicity is common in clinical studies and the current standard is to use the familywise error rate to ensure that the errors are kept at a prespecified level. In this paper, we will show that, in certain situations, familywise error rate control does not account for all errors made. To counteract this problem, we propose the use of the expected number of false claims (EFC). We will show that a (weighted) Bonferroni approach can be used to control the EFC, discuss how a study that uses the EFC can be powered for co-primary, exchangeable, and hierarchical endpoints, and show how the weight for the weighted Bonferroni test can be determined in this manner. ©2016 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.
多重性在临床研究中很常见，当前的标准是使用族错误率来确保错误保持在预先设定的水平。在本文中，我们将表明，在某些情况下，族错误率控制并不能涵盖所有发生的错误。为了解决这个问题，我们建议使用错误声明预期数量（EFC）。我们将表明可以使用（加权）邦费罗尼方法来控制EFC，讨论使用EFC的研究如何针对共同主要、可交换和分层终点进行效能分析，并展示如何以这种方式确定加权邦费罗尼检验的权重。©2016作者。《药物统计学》由约翰·威利父子有限公司出版。
