A Semiparametric Marginalized Model for Longitudinal Data with Informative Dropout.
A Semiparametric Marginalized Model for Longitudinal Data with Informative Dropout.
复制标题
具有信息丢失的纵向数据的半参数边缘化模型。
DOI：
10.1155/2012/734341
复制
发表时间：
2012
影响因子：
1.1
通讯作者：
Lu,Wenbin
中科院分区：
文献类型：
--
作者：
Liu,Mengling;Lu,Wenbin
我们提出了一种边缘联合建模方法，当纵向测量受到信息缺失的影响时，可以对纵向响应和协变量之间的关联进行边缘推断。所提出的模型的动机是通过潜在变量将纵向响应和退出时间联系起来，同时关注边际推断。我们开发了一个基于一系列估计方程的简单推理过程，并且得到的估计量是一致的和渐近正态的，并且具有一个三明治型协方差矩阵，可以用通常的插入规则估计。我们的方法的性能通过模拟评估，并与肾脏疾病的数据应用说明。
We propose a marginalized joint‐modeling approach for marginal inference on the association between longitudinal responses and covariates when longitudinal measurements are subject to informative dropouts. The proposed model is motivated by the idea of linking longitudinal responses and dropout times by latent variables while focusing on marginal inferences. We develop a simple inference procedure based on a series of estimating equations, and the resulting estimators are consistent and asymptotically normal with a sandwich‐type covariance matrix ready to be estimated by the usual plug‐in rule. The performance of our approach is evaluated through simulations and illustrated with a renal disease data application.