论文标题
缺少多级序数结果的多种插补方法
Multiple Imputation Methods for Missing Multilevel Ordinal Outcomes
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Multiple imputation (MI) is an established technique to handle missing data in observational studies. Joint modeling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel clustered data. However, MI approaches for ordinal clustered outcome variables have not been well studied, especially when there is informative cluster size (ICS). The purpose of this study is to describe different imputation and analysis strategies for the multilevel ordinal outcome when ICS exists. We conducted comprehensive Monte Carlo simulation studies to compare five different methods: complete case analysis (CCA), FCS, FCS+CS (include cluster size (CS) when performing the imputation), JM, and JM+CS under different scenarios. We evaluated their performances using an proportional odds logistic regression model estimated with cluster weighted generalized estimating equations (CWGEE). The simulation results show that including cluster size in imputation can significantly improve imputation accuracy when ICS exists. FCS provides more accurate and robust estimation than JM, followed by CCA for multilevel ordinal outcomes. We further applied those methods to a real dental study.
