An Overview of Longitudinal Data Analysis Methods for Neurological Research

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the depen...

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Published inDementia and geriatric cognitive disorders extra Vol. 1; no. 1; pp. 330 - 357
Main Authors Locascio, Joseph J., Atri, Alireza
Format Journal Article
LanguageEnglish
Published Basel, Switzerland S. Karger AG 26.10.2011
Karger Publishers
Subjects
Analysis
Longitudinal studies
Methods
Neurology
Review
Review Article
Statistics
Neurology
Longitudinal studies
Statistics
Analysis
Methods
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Summary:The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models.
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ISSN:1664-5464
1664-5464
DOI:10.1159/000330228