Common predictor effects for multivariate longitudinal data.

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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Jia J,Weiss RE

doi

10.1002/sim.3589

subject

Has Abstract

pub_date

2009-06-15 00:00:00

pages

1793-804

issue

13

eissn

0277-6715

issn

1097-0258

journal_volume

28

pub_type

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