A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data
A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data
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DOI：
10.1002/sim.3456
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发表时间：
2008-12-30
影响因子：
2
通讯作者：
Ohashi, Yasuo
中科院分区：
文献类型：
--
作者：
Funatogawa, Ikuko;Funatogawa, Takashi;Ohashi, Yasuo
在临床研究中，依赖性双变量连续响应可能会随着时间的推移接近平衡。我们提出了一个双变量纵向数据的自回归线性混合效应模型，其中当前的响应是对两个变量，固定效应和随机效应的先前响应进行回归的。使用固定效应和随机效应对均衡进行建模。该模型是Funatogawa等人（Statist. 2007; 26：2113-2130）。作为该方法的一个例证，我们分析了慢性血液透析患者继发性甲状旁腺功能亢进治疗中甲状旁腺激素和血清钙的测量。版权所有（C）2008约翰威利父子有限公司
In clinical studies, dependent bivariate continuous responses may approach equilibrium over time. We propose an autoregressive linear mixed effects model for bivariate longitudinal data in which the current responses are regressed on the previous responses of both variables, fixed effects, and random effects. The equilibria are modeled using fixed and random effects. This model is a bivariate extension of the model for univariate longitudinal data given by Funatogawa et al. (Statist. Med. 2007; 26:2113-2130). As an illustration of the approach we analyze parathyroid hormone and serum calcium measurements in the treatment of secondary hyperparathyroidism in chronic hemodialysis patients. Copyright (C) 2008 John Wiley & Sons, Ltd.