Common predictor effects for multivariate longitudinal data.

Full Abstract

Multivariate outcomes measured longitudinally over time are common in medicine, public health, psychology and sociology. The typical (saturated) longitudinal multivariate regression model has a separate set of regression coefficients for each outcome. However, multivariate outcomes are often quite similar and many outcomes can be expected to respond similarly to changes in covariate values. Given a set of outcomes likely to share common covariate effects, we propose the clustered outcome common predictor effect model and offer a two step iterative algorithm to fit the model using available software for univariate longitudinal data. Outcomes that share predictor effects need not be chosen a priori; we propose model selection tools to let the data select outcome clusters. We apply the proposed methods to psychometric data from adolescent children of HIV+ parents. Copyright (c) 2009 John Wiley & Sons, Ltd.

Author information

Author/s: Jia, Juan (J); Weiss, Robert E (RE);

Affiliation: Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095-1772, U.S.A.

Grants: P30MH58107 (Agency:NIMH NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-Jun; vol 28 (issue 13) : pp 1793-804

Dates: Created 2009/05/13; Completed 2009/07/01;

PMID: 19360840, status: MEDLINE (last retrieval date: 7/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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