Multiple imputation methods for the missing covariates in generalized estimating equation
Multiple imputation methods for the missing covariates in generalized estimating equation
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DOI：
10.2307/2533521
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发表时间：
1997-12-01
期刊：
影响因子：
1.9
通讯作者：
Paik, MC
中科院分区：
文献类型：
--
作者：
Xie, F;Paik, MC
本文讨论了广义估计方程（GEE）模型中协变量缺失问题。通过仿真和算例，对各种多重插值方法（MI）的估计进行了检验，并与样本平均插值方法（SA）进行了比较。仿真结果表明，在正确的模型规范下，MI估计器的偏差可以忽略不计，并且与SA估计器具有相当相似的效率。MI估计的一个实际优点是，标准误差比SA估计更容易计算。
This paper discusses the missing covariates problem in the generalized estimating equation (GEE) model. Estimates by various multiple imputation techniques (MI) are examined and compared to the sample average imputation method (SA) through simulations and an example. The simulation results show that, under the correct model specification, the MI estimators have negligible bias and have fairly similar efficiencies as the SA estimator. A practical advantage of the MI estimates is that the standard errors can be more easily computed than the SA estimates.