Identifying latent clusters of variability in longitudinal data.

Identifying latent clusters of variability in longitudinal data. Research Abstract Details 

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Identifying latent clusters of variability in longitudinal data. Abstract Text:

Michael R Elliott,Michael R Elliott,Michael R Elliott,

Means or other central tendency measures are by far the most common focus of statistical analyses. However, as Carroll (2003) noted, "systematic dependence of variability on known factors" may be "fundamental to the proper solution of scientific problems" in certain settings. We develop a latent cluster model that relates underlying "clusters" of variability to baseline or outcome measures of interest. Because estimation of variability is inextricably linked to estimation of trend, assumptions about underlying trends are minimized by using nonparametric regression estimates. The resulting residual errors are then clustered into unobserved clusters of variability that are in turn related to subject-level predictors of interest. An application is made to psychological affect data.

Identifying latent clusters of variability in longitudinal data. Publishing Authors By Initials

MR Elliott,MR Elliott,MR Elliott,

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Identifying latent clusters of variability in longitudinal data. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Biostatistics (Oxford, England)

VOLUME: 8

Page Numbers: 756-71

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ISSN: 1465-4644

DAY: 30

MONTH: 01

YEAR: 2007

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LANGUAGE: eng

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AFFILIATION: Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA and Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48106, USA. mrelliot@umich.edu.

Country: England

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MEDLINETA: Biostatistics

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