When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts.
When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts.
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DOI：
10.1186/s12874-017-0442-1
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发表时间：
2017-12-06
影响因子：
4
通讯作者：
Winkel P
中科院分区：
文献类型：
--
作者：
Jakobsen JC;Gluud C;Wetterslev J;Winkel P
丢失的数据可能会严重损害随机临床试验的推论，特别是如果丢失的数据没有得到适当的处理。由于数据缺失造成的潜在偏差取决于导致数据缺失的机制，以及应用来修正缺失的分析方法。因此，对有缺失值的试验数据的分析需要仔细规划和注意。作者们举行了几次会议和讨论，考虑处理丢失数据的最佳方法，以将偏差潜力降至最低。我们还在PubMed(关键词：缺失数据；随机*；统计分析)和已知研究的参考文献列表中搜索论文(理论论文；实证研究；模拟研究等)。在分析随机临床试验时如何处理丢失的数据。在分析随机临床试验的结果时，处理丢失的数据是一项重要但困难和复杂的任务。我们考虑如何在随机临床试验的规划阶段优化缺失数据的处理，并推荐分析方法，以防止不可避免的缺失数据造成的偏差。我们考虑了使用最佳-最差和最差-最佳灵敏度分析、多重归因法和全信息最大似然法的优点和局限性。我们还提供了如何处理缺失数据的实用流程图，并概述了在试验的分析阶段总是需要考虑的步骤。我们提出了一个实用的指南和流程图，描述了在随机临床中，何时以及如何使用多重补偿来处理丢失的数据。本文的在线版本(10.1186/s12874-0170442-1)包含补充材料，可供授权用户使用。
Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention. The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials. Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. The online version of this article (10.1186/s12874-017-0442-1) contains supplementary material, which is available to authorized users.
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影响因子：
14.6
作者：
Groenwold, Rolf H. H.;Moons, Karel G. M.;Vandenbroucke, Jan P.
通讯作者：
Vandenbroucke, Jan P.
影响因子：
105.7
作者：
Hrobjartsson, Asbjorn;Thomsen, Ann Sofia Skou;Brorson, Stig
通讯作者：
Brorson, Stig
影响因子：
1.1
作者：
Birhanu, Teshome;Molenberghs, Geert;Kenward, Michael G.
通讯作者：
Kenward, Michael G.
影响因子：
4
作者：
Kahan BC
通讯作者：
Kahan BC
影响因子：
14.6
作者：
Hrobjartsson, Asbjorn;Thomsen, Ann Sofia Skou;Brorson, Stig
通讯作者：
Brorson, Stig