Ageing Research Unit, Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia.
J Clin Epidemiol. 2011 Jul;64(7):787-93. doi: 10.1016/j.jclinepi.2010.10.011. Epub 2011 Feb 2.
The Mini-Mental State Examination (MMSE) is used to estimate current cognitive status and as a screen for possible dementia. Missing item-level data are commonly reported. Attention to missing data is particularly important. However, there are concerns that common procedures for dealing with missing data, for example, listwise deletion and mean item substitution, are inadequate.
We used multiple imputation (MI) to estimate missing MMSE data in 17,303 participants who were drawn from the Dynamic Analyses to Optimize Aging project, a harmonization project of nine Australian longitudinal studies of aging.
Our results indicated differences in mean MMSE scores between those participants with and without missing data, a pattern consistent over age and gender levels. MI inflated MMSE scores, but differences between those imputed and those without missing data still existed. A simulation model supported the efficacy of MI to estimate missing item level, although serious decrements in estimation occurred when 50% or more of item-level data were missing, particularly for the oldest participants.
Our adaptation of MI to obtain a probable estimate for missing MMSE item level data provides a suitable method when the proportion of missing item-level data is not excessive.
简易精神状态检查（MMSE）用于评估当前认知状态，并作为可能痴呆的筛查工具。常见的是缺失项目水平数据。对缺失数据的关注尤为重要。然而，人们担心处理缺失数据的常见程序，例如完全删除和平均项目替换，是不充分的。
我们使用多重插补（MI）来估计来自动态分析优化老龄化项目（Dynamic Analyses to Optimize Aging project）的 17303 名参与者的 MMSE 缺失数据，该项目是澳大利亚九个老龄化纵向研究的协调项目。
我们的结果表明，在有和没有缺失数据的参与者之间，MMSE 得分的平均值存在差异，这种模式在年龄和性别水平上都是一致的。MI 夸大了 MMSE 得分，但在那些有和没有缺失数据的参与者之间仍然存在差异。模拟模型支持 MI 对缺失项目水平进行估计的有效性，尽管当 50%或更多的项目水平数据缺失时，估计严重下降，尤其是对于最年长的参与者。
我们对 MI 的改编，以获得缺失 MMSE 项目水平数据的可能估计值，在缺失项目水平数据的比例不过高的情况下提供了一种合适的方法。
