Generalized Linear Mixed Effects Model in the Analysis of Longitudinal Discrete Data
Generalized Linear Mixed Effects Model in the Analysis of Longitudinal Discrete Data
复制标题
纵向离散数据分析中的广义线性混合效应模型
DOI：
10.1007/978-3-642-34904-1_11
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. S. Cabral
中科院分区：
文献类型：
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作者：
Eunice Carrasquinha;M. H. Gonçalves;M. S. Cabral
在许多癌症研究和临床研究中，随着时间的推移，对一个或多个治疗组中的每个受试者重复观察反应变量。此类研究通常称为纵向研究，并且对每个向量响应的重复观察可能是相关的。重复数据的自相关结构在此类数据的分析中起着重要作用。广义线性混合效应模型（GLMM）是用于分析离散纵向数据的方法之一，其中线性预测变量中随机效应的使用解释了受试者内的关联。本章的目标是在纵向离散数据分析中引入该模型，同时考虑到理论和计算困难以及与参数解释相关的问题。该方法通过分析包含致癌实验中肿瘤数量纵向测量的数据集来说明，以研究脂质对乳腺癌发展的影响。库 lme4[Bates, D.、Maechler, M.、Bolker, B.：lme4：使用 S4 类的线性混合效应模型。 R 包版本 0.999375-39。 http://CRAN.R-project.org/package=lme4 (2011)]使用R软件。
In many cancer studies and clinical research, repeated observations of response variables are taken over time for each subject in one or more treatment groups. Such research is commonly referred to longitudinal studies and the repeated observations of each vector response are likely to be correlated. The autocorrelation structure for the repeated data plays a significant role in the analysis of such data. The generalized linear mixed effects model (GLMM) is one of the approaches used to analyze discrete longitudinal data, where the use of random effects in the linear predictor accounts for the within-subject association. The goal of this chapter is to introduce this model in the analysis of longitudinal discrete data, taking into account the theoretical and computational difficulties as well as the problems related to parameters interpretation. The methodology is illustrated by analyzing data sets containing longitudinal measures of number of tumors in an experiment of carcinogenesis to study the influence of lipids in the development of breast cancer. The librarylme4[Bates, D., Maechler, M., Bolker, B.: lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-39. http://CRAN.R-project.org/package=lme4 (2011)] inRsoftware is used.