Multivariate modeling of two associated cognitive outcomes in a longitudinal study

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Abstract

Longitudinal studies of Alzheimer's disease provide information about cognitive decline and predictors of this decline. However, overall cognitive function is comprised of many underlying processes, each of which may respond differently over time and may be affected by different predictors. In addition to studying how these processes decline independently, one might also be interested in how the processes decline together. Multivariate growth models, an extension and modification of random effects models, provide a means of dealing with these issues and enable assessing the association between the processes of interest. This technique allows for separate random effects and predictors for each process in the same model, thereby providing simultaneous estimates of the model parameters and variability for each process. We can then determine if factors associated with decline in one process are also associated with decline in another process and the extent to which the processes differ. We provide data that include information on two underlying processes of cognitive function, namely memory and executive function, to illustrate this methodology.

Original languageEnglish (US)
Pages (from-to)357-365
Number of pages9
JournalJournal of Alzheimer's Disease
Volume5
Issue number5
StatePublished - Oct 2003

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Cognition
Longitudinal Studies
Executive Function
Alzheimer Disease
Growth
Cognitive Dysfunction

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology

Cite this

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abstract = "Longitudinal studies of Alzheimer's disease provide information about cognitive decline and predictors of this decline. However, overall cognitive function is comprised of many underlying processes, each of which may respond differently over time and may be affected by different predictors. In addition to studying how these processes decline independently, one might also be interested in how the processes decline together. Multivariate growth models, an extension and modification of random effects models, provide a means of dealing with these issues and enable assessing the association between the processes of interest. This technique allows for separate random effects and predictors for each process in the same model, thereby providing simultaneous estimates of the model parameters and variability for each process. We can then determine if factors associated with decline in one process are also associated with decline in another process and the extent to which the processes differ. We provide data that include information on two underlying processes of cognitive function, namely memory and executive function, to illustrate this methodology.",
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