The Generalized Estimating Equation Approach When Data Are Not Missing Completely at Random
提出了均值插补和多重插补两种方法处理广义估计方程分析中的缺失数据，适用于数据随机缺失的情况，并应用于中风研究中的神经学结局数据。
Abstract We propose two methods for handling missing data in generalized estimating equation (GEE) analyses: mean imputation and multiple imputation. Each provides valid GEE estimates when data are missing at random. Missing outcomes are imputed sequentially starting from the outcome nearest in time to the observed outcome. The estimators from the two kinds of imputation are compared with the weighting method of Robins et al. We show that multiple imputation with an infinite number of replications is asymptotically equivalent to mean imputation. The methods are applied to a stroke study in which neurological outcomes are measured over time after stroke but some outcomes are missing due to death or loss to follow up.