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Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements
Journal of Multivariate Analysis ( IF 1.4 ) Pub Date : 2016-01-01 , DOI: 10.1016/j.jmva.2015.08.020
Rolando De la Cruz 1 , Cristian Meza 2 , Ana Arribas-Gil 3 , Raymond J Carroll 4
Affiliation  

Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification.

中文翻译:

具有来自非线性纵向测量的随机效应协变量的数据的贝叶斯回归分析

多种响应变量和纵向测量的联合模型由混合效应模型组成,以拟合纵向轨迹,其随机效应作为协变量进入初级响应的广义线性模型中。它们提供了一种评估这两种数据之间关联的有用方法,在临床研究中,这些数据通常是针对一系列个体联合收集的,可能有助于理解例如某种疾病的康复机制或特定疾病的疗效。治疗。当非线性混合效应模型用于拟合纵向轨迹时,基于似然近似的现有估计策略已显示出一些计算效率问题(De la Cruz 等,2011)。在本文中,我们考虑联合模型的贝叶斯估计程序,其中包含用于纵向数据的非线性混合效应模型和用于初级响应的广义线性模型。提议的先验结构允许实现 MCMC 采样器。此外,我们认为纵向模型中的误差可能是相关的。我们将我们的方法应用于分析妊娠早期测量的激素水平,可用于预测正常与异常妊娠结果。我们还进行了一项模拟研究,以评估建模相关错误的重要性并量化模型指定错误的后果。提议的先验结构允许实现 MCMC 采样器。此外,我们认为纵向模型中的误差可能是相关的。我们将我们的方法应用于分析妊娠早期测量的激素水平,可用于预测正常与异常妊娠结果。我们还进行了一项模拟研究,以评估建模相关错误的重要性并量化模型指定错误的后果。提议的先验结构允许实现 MCMC 采样器。此外,我们认为纵向模型中的误差可能是相关的。我们将我们的方法应用于分析妊娠早期测量的激素水平,可用于预测正常与异常妊娠结果。我们还进行了一项模拟研究,以评估建模相关错误的重要性并量化模型指定错误的后果。
更新日期:2016-01-01
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