Little R J, Raghunathan T
Department of Biostatistics and Institute for Social Research, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA.
Stat Med. 1999;18(17-18):2465-78. doi: 10.1002/(sici)1097-0258(19990915/30)18:17/18<2465::aid-sim269>3.0.co;2-2.
Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. A simple and convenient approach to analysis is to develop summary measures for each individual and then regress the summary measures on between-subject covariates. We examine properties of this approach in the context of the linear mixed effects model when the data are not missing completely at random, in the sense that drop-out depends on the values of the repeated measures after conditioning on fixed covariates. The approach is compared with likelihood-based approaches that model the vector of repeated measures for each individual. Methods are compared by simulation for the case where repeated measures over time are linear and can be summarized by a slope and intercept for each individual. Our simulations suggest that summary measures analysis based on the slopes alone is comparable to full maximum likelihood when the data are missing completely at random but is markedly inferior when the data are not missing completely at random. Analysis discarding the incomplete cases is even worse, with large biases and very poor confidence coverage.
受试者常常过早退出纵向研究，导致数据不均衡，每个受试者的测量次数不等。一种简单便捷的分析方法是为每个个体制定汇总测量指标，然后将汇总测量指标对个体间协变量进行回归分析。当数据并非完全随机缺失时，即在剔除固定协变量的影响后，退出情况取决于重复测量值，我们在这种情况下研究线性混合效应模型中该方法的性质。将该方法与为每个个体的重复测量向量建模的基于似然的方法进行比较。针对随时间的重复测量呈线性且可由每个个体的斜率和截距汇总的情况，通过模拟对方法进行比较。我们的模拟表明，当数据完全随机缺失时，仅基于斜率的汇总测量指标分析与完全最大似然法相当，但当数据并非完全随机缺失时则明显较差。舍弃不完整病例的分析更糟糕，存在较大偏差且置信区间覆盖效果很差。
