期刊
STATISTICS IN MEDICINE
卷 28, 期 2, 页码 221-239出版社
WILEY-BLACKWELL
DOI: 10.1002/sim.3478
关键词
linear mixed model; generalized linear mixed model; random effects; fixed effects; robust variance; conditional maximum likelihood; Hausman test; CES-D
类别
资金
- Agency tor Healthcare Research and Quality [1R01 HS14206]
- Maternal and Child Health Bureaul [MCJ-260743]
- Health Resources and Services Administration
- Department of Health and Human Servicesf
For analyses of longitudinal repeated-measures data, statistical methods include the random effects model, fixed effect, model and the method of generalized estimating equations. We examine the assumptions that underlie these approaches to assessing covariate effects oil the mean of a continuous, dichotomous or count outcome. Access to statistical software to implement these models has led to widespread application in numerous disciplines. However, careful consideration should be paid to their critical assumptions to ascertain which model might be appropriate in a given setting. To illustrate similarities and differences that might exist in empirical results, we use a study that assessed depressive symptoms in low-income pregnant women using a structured instrument with up to five assessments that spanned the pre-natal and post-natal periods. Understanding the conceptual differences between the methods is important in their proper application even though empirically they might not differ substantively. The choice of model in specific applications would depend on the relevant questions being addressed, which ill turn informs the type of design ana data collection that would be relevant. Copyright (C) 2008 John Wiley & Sons, Ltd.
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