Advances in Analysis of Longitudinal Data

被引：366
作者：
Gibbons, Robert D. [1 ,2 ]
Hedeker, Donald [1 ,2 ]
DuToit, Stephen [1 ,3 ]
机构：
[1] Univ Illinois, Ctr Hlth Stat, Chicago, IL 60612 USA
[2] Univ Illinois, Div Epidemiol & Biostat, Chicago, IL 60612 USA
[3] Sci Software Int, Lincolnwood, IL 60712 USA
来源：
ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 6 | 2010年 / 6卷
关键词：
mixed-effects models; logistic regression; Poisson regression; marginal maximum likelihood; generalized estimating equations; multilevel models; EMPIRICAL BAYES ESTIMATION; EFFECTS REGRESSION-MODEL; SAMPLE-SIZE; MIXED MODELS; MISSING DATA; SMOKING; GROWTH; ATTRITION; SELECTION; DISCRETE;
D O I：
10.1146/annurev.clinpsy.032408.153550
中图分类号：
B849 [应用心理学];
学科分类号：
040203 ;
摘要：
In this review, we explore recent developments in the area of linear and nonlinear generalized mixed-effects regression models and various alternatives, including generalized estimating equations for analysis of longitudinal data Methods are described for continuous and normally distributed as well as categorical (binary, ordinal, nominal) and count (Poisson) variables. Extensions of the model to three and four levels of clustering, multivariate outcomes, and incorporation of design weights are also described. Linear and nonlinear models are illustrated using an example involving a study of the relationship between mood and smoking.
引用
收藏
页码：79 / 107
页数：29
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