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Stat Med. 2009 Jun 15;28(13):1793-804. doi: 10.1002/sim.3589.

Common predictor effects for multivariate longitudinal data.

Author information

1
Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095-1772, U.S.A.

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.

PMID:
19360840
PMCID:
PMC3896128
DOI:
10.1002/sim.3589
[Indexed for MEDLINE]
Free PMC Article

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