We propose a random-effects approach to missing values for linear mixed model (LMM) analysis. The method converts a LMM with missing covariates to another LMM without missing covariates. The standard LMM analysis tools for longitudinal data then apply. Performance of the method is evaluated empirically, and compared with alternative approaches, including the popular MICE procedure of multiple imputation. Theoretical explanations are given for the patterns observed in the simulation studies. A real-data example is discussed.

翻译：我们提出了一种针对线性混合模型（LMM）分析中缺失值的随机效应方法。该方法将含有缺失协变量的LMM转换为另一个不含缺失协变量的LMM。随后，标准的纵向数据LMM分析工具即可适用。我们通过实证评估了该方法的性能，并与包括流行的多重插补MICE程序在内的替代方法进行了比较。针对模拟研究中观察到的模式，我们给出了理论解释。文中还讨论了一个真实数据的示例。