Attrition in longitudinal studies: How to deal with missing data
Attrition in longitudinal studies: How to deal with missing data
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DOI：
10.1016/s0895-4356(01)00476-0
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发表时间：
2002-04-01
影响因子：
7.2
通讯作者：
de Vente, W
中科院分区：
文献类型：
--
作者：
Twisk, J;de Vente, W
本文的目的是说明缺失数据对纵向统计分析[即重复测量的多元方差分析和广义估计方程（GEE）]结果的影响，并说明使用不同插补方法替换缺失数据的影响。除了完整的数据集之外，还考虑了四个不完整的数据集：两个缺失数据 10% 的数据集和两个缺失数据 25% 的数据集。在这两种情况下，缺失被认为是独立的并且依赖于观察到的数据。插补方法分为横截面方法（即系列平均值、热甲板和横截面回归）和纵向方法（即最后值结转、纵向插值和纵向回归）。除此之外，还对多重插补方法进行了应用和讨论。分析是在特定的（观察）纵向数据集上进行的，具有特定的缺失数据模式和插补方法。该图的结果表明，当使用重复测量的多元方差分析时，强烈推荐插补方法（因为在所使用的软件中实现多元方差分析，使用列表删除具有缺失值的案例）。应用 GEE 分析，不需要插补方法。当使用插补方法时，纵向插补方法通常优于横截面插补方法，因为点估计和标准误差更接近从完整数据集得出的估计。此外，本研究表明，理论上更有效的多重插补方法不会比更简单的（纵向）插补方法导致不同的点估计，但是，估计的标准误差在理论上似乎更充分，因为它们反映了由缺失值引起的估计的不确定性。 (C) 2002 Elsevier Science Inc. 保留所有权利。
The purpose of this paper was to illustrate the influence of missing data on the results of longitudinal statistical analyses [i.e., MANOVA for repeated measurements and Generalised Estimating Equations (GEE)] and to illustrate the influence of using different imputation methods to replace missing data. Besides a complete dataset, four incomplete datasets were considered: two datasets with 10% missing data and two datasets with 25% missing data. In both situations missingness was considered independent and dependent on observed data. Imputation methods were divided into cross-sectional methods (i.e., mean of series, hot deck, and cross-sectional regression) and longitudinal methods (i.e., last value carried forward, longitudinal interpolation, and longitudinal regression). Besides these, also the multiple imputation method was applied and discussed. The analyses were performed on a particular (observational) longitudinal dataset, with particular missing data patterns and imputation methods. The results of this illustration shows that when MANOVA for repeated measurements is used, imputation methods are highly recommendable (because MANOVA as implemented in the software used, uses listwise deletion of cases with a missing value). Applying GEE analysis, imputation methods were not necessary. When imputation methods were used, longitudinal imputation methods were often preferable ab9ove cross-sectional imputation methods, in a way that the point estimates and standard errors were closer to the estimates derived from the complete dataset. Furthermore, this study showed that the theoretically more valid multiple imputation method did not lead to different point estimates than the more simple (longitudinal) imputation methods, However, the estimated standard errors appeared to be theoretically more adequate, because they reflect the uncertainty in estimation caused by missing values. (C) 2002 Elsevier Science Inc. All rights reserved.