Zhang Hui, Paik Myunghee Cho
Department of Statistics, Pfizer (China) Research and Development Center, Shanghai, China.
J Biopharm Stat. 2009 Nov;19(6):1001-17. doi: 10.1080/10543400903242761.
In longitudinal studies, missingness of data is often unavoidable. Valid estimators from the generalized linear mixed model usually rely on the correct specification of the missing data mechanism. An incorrectly specified missing mechanism may lead to a biased estimator. In this article, we propose a class of unbiased estimating equations using pairwise conditional technique to deal with the generalized linear mixed model under benign non-ignorable missingness where specification of the missing model is not needed. We show that the proposed estimator is consistent and asymptotically normal under certain conditions. Simulation results and an example using longitudinal course of neuropsychological data are also shown.
在纵向研究中，数据缺失往往不可避免。广义线性混合模型的有效估计量通常依赖于缺失数据机制的正确设定。错误设定的缺失机制可能导致估计量有偏差。在本文中，我们提出了一类使用成对条件技术的无偏估计方程，以处理在良性非可忽略缺失情况下的广义线性混合模型，此时不需要设定缺失模型。我们表明，在某些条件下，所提出的估计量是一致的且渐近正态的。还展示了模拟结果以及一个使用神经心理学数据纵向过程的示例。
