Huang Yangxin, Dagne Getachew
Department of Epidemiology & Biostatistics, College of Public Health, University of South Florida, Tampa, Florida 33612, USA.
Biometrics. 2012 Sep;68(3):943-53. doi: 10.1111/j.1541-0420.2011.01719.x. Epub 2011 Dec 7.
It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications.
使用具有正态分布的半参数非线性混合效应（SNLME）模型来分析复杂的纵向数据是一种常见的做法。模型误差的正态性假设可能会不切实际地掩盖个体变异的重要特征。为了部分解释个体间和个体内的变异，通常会在此类模型中引入协变量，但有些协变量的测量可能存在大量误差。此外，响应可能会缺失，且缺失情况可能不可忽视。当观察到具有偏度、缺失值和测量误差的数据时，推断过程可能会显著复杂化。在文献中，人们对在这类模型中处理偏度、不完整性或协变量测量误差中的任何一个都有相当大的兴趣，但同时考虑所有这三个特征的研究相对较少。在本文中，我们的目标是通过基于灵活的贝叶斯SNLME模型对响应和协变量过程进行联合建模，来解决偏度、缺失值和协变量测量误差的同时影响。使用一个真实的艾滋病数据集来说明该方法，以比较具有各种情景和不同分布规范的潜在模型。
