Shih Weichung
Division of Biometrics, University of Medicine and Dentistry of New Jersey School of Public Health, New Brunswick, New Jersey, USA.
Curr Control Trials Cardiovasc Med. 2002 Jan 8;3(1):4. doi: 10.1186/1468-6708-3-4.
Acommon problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.
临床试验中的一个常见问题是出现缺失数据,即患者未完成研究且未进行进一步测量就退出。缺失数据会导致对完整或所有可用数据进行的常规统计分析产生偏差。目前尚无普遍适用的处理缺失数据的方法。我们建议如下:(1)报告各治疗组的退出原因及比例;(2)进行敏感性分析以涵盖不同的假设情况,并讨论它们之间的一致性或差异;(3)在设计阶段和试验监测期间注意尽量减少退出的可能性;(4)如果可能,收集关于主要终点的退出后数据;以及(5)在有许多退出情况的研究中,将退出事件本身视为一个重要终点。