Yucel Recai M, Demirtas Hakan
Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place Room 139, Rensselaer, NY 12144, United States.
Comput Stat Data Anal. 2010 Mar 1;54(3):790-801. doi: 10.1016/j.csda.2009.01.016.
Multivariate extensions of well-known linear mixed-effects models have been increasingly utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The normality assumption for the underlying error terms and random effects plays a crucial role in simulating the posterior predictive distribution from which the multiple imputations are drawn. The plausibility of this normality assumption on the subject-specific random effects is assessed. Specifically, the performance of multiple imputation created under a multivariate linear mixed-effects model is investigated on a diverse set of incomplete data sets simulated under varying distributional characteristics. Under moderate amounts of missing data, the simulation study confirms that the underlying model leads to a well-calibrated procedure with negligible biases and actual coverage rates close to nominal rates in estimates of the regression coefficients. Estimation quality of the random-effect variance and association measures, however, are negatively affected from both the misspecification of the random-effect distribution and number of incompletely-observed variables. Some of the adverse impacts include lower coverage rates and increased biases.
著名线性混合效应模型的多变量扩展在多级不完全数据的分析中，通过多重填补进行推断时越来越多地被使用。潜在误差项和随机效应的正态性假设在模拟后验预测分布中起着关键作用，而多重填补正是从该分布中抽取的。评估了关于个体特定随机效应的这种正态性假设的合理性。具体而言，在具有不同分布特征的各种不完全数据集上，研究了在多变量线性混合效应模型下创建的多重填补的性能。在中等程度的缺失数据情况下，模拟研究证实，基础模型会产生一个校准良好的程序，在回归系数估计中偏差可忽略不计，实际覆盖率接近名义覆盖率。然而，随机效应方差和关联度量的估计质量受到随机效应分布的错误设定和不完全观测变量数量的负面影响。一些不利影响包括较低的覆盖率和偏差增加。
