Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Stat Med. 2012 Dec 10;31(28):3433-43. doi: 10.1002/sim.5519. Epub 2012 Jul 25.
This article summarizes recommendations on the design and conduct of clinical trials of a National Research Council study on missing data in clinical trials. Key findings of the study are that (a) substantial missing data is a serious problem that undermines the scientific credibility of causal conclusions from clinical trials; (b) the assumption that analysis methods can compensate for substantial missing data is not justified; hence (c) clinical trial design, including the choice of key causal estimands, the target population, and the length of the study, should include limiting missing data as one of its goals; (d) missing-data procedures should be discussed explicitly in the clinical trial protocol; (e) clinical trial conduct should take steps to limit the extent of missing data; (f) there is no universal method for handling missing data in the analysis of clinical trials - methods should be justified on the plausibility of the underlying scientific assumptions; and (g) when alternative assumptions are plausible, sensitivity analysis should be conducted to assess robustness of findings to these alternatives. This article focuses on the panel's recommendations on the design and conduct of clinical trials to limit missing data. A companion paper addresses the panel's findings on analysis methods.
本文总结了美国国家研究理事会(National Research Council)关于临床试验中缺失数据的研究的设计和实施建议。该研究的主要发现是:(a)大量数据缺失是一个严重的问题,会削弱临床试验因果结论的科学可信度;(b)分析方法可以弥补大量数据缺失的假设是没有依据的;因此(c)临床试验设计,包括关键因果估计量的选择、目标人群和研究长度,都应将限制数据缺失作为目标之一;(d)缺失数据程序应在临床试验方案中明确讨论;(e)临床试验实施应采取措施限制缺失数据的程度;(f)在临床试验的分析中,没有处理缺失数据的通用方法——方法应基于潜在科学假设的合理性进行论证;(g)当替代假设合理时,应进行敏感性分析以评估这些替代方案对研究结果的稳健性。本文重点介绍了专家组关于限制缺失数据的临床试验设计和实施的建议。一篇配套论文介绍了专家组关于分析方法的研究结果。