Yi Grace Y, He Wenqing
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
Biometrics. 2009 Jun;65(2):618-25. doi: 10.1111/j.1541-0420.2008.01105.x.
Recently, median regression models have received increasing attention. When continuous responses follow a distribution that is quite different from a normal distribution, usual mean regression models may fail to produce efficient estimators whereas median regression models may perform satisfactorily. In this article, we discuss using median regression models to deal with longitudinal data with dropouts. Weighted estimating equations are proposed to estimate the median regression parameters for incomplete longitudinal data, where the weights are determined by modeling the dropout process. Consistency and the asymptotic distribution of the resultant estimators are established. The proposed method is used to analyze a longitudinal data set arising from a controlled trial of HIV disease (Volberding et al., 1990, The New England Journal of Medicine 322, 941-949). Simulation studies are conducted to assess the performance of the proposed method under various situations. An extension to estimation of the association parameters is outlined.
最近，中位数回归模型受到了越来越多的关注。当连续响应遵循与正态分布有很大差异的分布时，通常的均值回归模型可能无法产生有效的估计量，而中位数回归模型可能表现令人满意。在本文中，我们讨论使用中位数回归模型来处理带有缺失值的纵向数据。提出了加权估计方程来估计不完整纵向数据的中位数回归参数，其中权重通过对缺失过程进行建模来确定。建立了所得估计量的一致性和渐近分布。所提出的方法用于分析来自一项HIV疾病对照试验（Volberding等人，1990年，《新英格兰医学杂志》322卷，941 - 949页）的纵向数据集。进行了模拟研究以评估所提出方法在各种情况下的性能。概述了对关联参数估计的扩展。
