Wittes Janet
Statistics Collaborative, Washington, DC, USA.
J Biopharm Stat. 2009 Nov;19(6):957-68. doi: 10.1080/10543400903239825.
Many methods are available to deal with missing data in randomized clinical trials, and active statistical research in the area continues. When, however, a high proportion of outcome data is missing, the methods can produce inaccurate estimates of the true effect size. This article argues that trialists should aim to minimize the proportion of missing data. To that end, the article suggests training investigators and study participants about the importance of completing the trial. It proposes language for informed consent documents, protocols, and case report forms that will distinguish between stopping study medication and removal from the trial itself.
在随机临床试验中,有多种方法可用于处理缺失数据,该领域的积极统计研究仍在继续。然而,当结局数据的缺失比例很高时,这些方法可能会对真实效应大小产生不准确的估计。本文认为,试验者应致力于将缺失数据的比例降至最低。为此,本文建议对研究者和研究参与者进行关于完成试验重要性的培训。它还为知情同意书、方案和病例报告表提供了相关措辞,以区分停止研究用药和退出试验本身。