Common predictor effects for multivariate longitudinal data.
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.
Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095-1772, U.S.A.
Juan
J
Jia
Robert E
RE
Weiss
D000293:Adolescent; D001699:Biometry / Q000379:methods*; D016241:Child of Impaired Parents / Q000523:psychology; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000523:psychology; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D000297:Psychology, Adolescent; D011594:Psychometrics / Q000706:statistics & numerical data