期刊文章%@ 1438-8871 %I Gunther Eysenbach %V 12 %N 5 %P e54 %T电子健康研究中的缺失数据方法:面向非数学研究人员的模拟研究和教程% a Blankers,Matthijs % a Koeter,Maarten W J % a Schippers,Gerard M %+阿姆斯特丹成瘾研究所(AIAR)，阿姆斯特丹大学学术医学中心，精神学系，PA 3.224室，邮政信箱22660，阿姆斯特丹，1100 DD，荷兰，31 20 891 37 54，m.blankers@amc.uva.nl %K数据缺失%K多重imputation %K Internet %K方法论%D 2010 %7 19.12.2010 %9原始论文%J J Med Internet Res %G英文%X背景:数据缺失是电子健康研究中常见的麻烦:很难预防，可能使研究结果无效。目的:本文对数据“缺失”的几种统计方法进行了讨论，并在模拟研究中进行了验证。本分析包括基本方法(完整案例分析、均值imputation和最后一次观测进行)和高级方法(期望最大化、回归imputation和多重imputation)，并讨论了优缺点。方法:用于模拟的数据集来自一项前瞻性队列研究，跟踪问题饮酒者在线自助项目的参与者。它包含124个非正态分布的端点，即研究对象的每日酒精消费计数。50%的病例在选定的变量中诱发了随机缺失(MAR)。通过执行自举模拟研究，计算了使用不同imputation方法获得的估计的有效性、可靠性和覆盖率。结果:在进行的仿真研究中，采用多重imputation技术得到了准确的结果。 Differences were found between the 4 tested multiple imputation programs: NORM, MICE, Amelia II, and SPSS MI. Among the tested approaches, Amelia II outperformed the others, led to the smallest deviation from the reference value (Cohen’s d = 0.06), and had the largest coverage percentage of the reference confidence interval (96%). Conclusions: The use of multiple imputation improves the validity of the results when analyzing datasets with missing observations. Some of the often-used approaches (LOCF, complete cases analysis) did not perform well, and, hence, we recommend not using these. Accumulating support for the analysis of multiple imputed datasets is seen in more recent versions of some of the widely used statistical software programs making the use of multiple imputation more readily available to less mathematically inclined researchers. %M 21169167 %R 10.2196/jmir.1448 %U //www.mybigtv.com/2010/5/e54/ %U https://doi.org/10.2196/jmir.1448 %U http://www.ncbi.nlm.nih.gov/pubmed/21169167

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