Skip Navigation


Biostatistics Advance Access originally published online on January 30, 2007
Biostatistics 2007 8(4):756-771; doi:10.1093/biostatistics/kxm003
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
8/4/756    most recent
kxm003v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Elliott, M. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Elliott, M. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Identifying latent clusters of variability in longitudinal data

Michael R. Elliott*

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{at}umich.edu

* To whom correspondence should be addressed.

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.

Keywords: Cubic spline; Heteroscedasticity; Longitudinal profiles; Nonparametric regression; Variance function

Received May 9, 2006; revised December 13, 2006; accepted for publication January 23, 2007.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 StrokeHome page
J. H. Pan, X. Y. Song, S. Y. Lee, and T. Kwok
Longitudinal Analysis of Quality of Life for Stroke Survivors Using Latent Curve Models
Stroke, October 1, 2008; 39(10): 2795 - 2802.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.