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Multivariate linear mixed models for multiple outcomes
Author(s) -
Sammel Mary,
Lin Xihong,
Ryan Louise
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990915/30)18:17/18<2479::aid-sim270>3.0.co;2-f
Subject(s) - multivariate statistics , correlation , statistics , contrast (vision) , multivariate analysis , linear model , mixed model , random effects model , latent variable , econometrics , mathematics , computer science , medicine , artificial intelligence , meta analysis , geometry
We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of exposure across outcomes. In contrast to the Sammel–Ryan model, the MLMM separates the mean and correlation parameters so that the mean estimation will remain reasonably robust even if the correlation is misspecified. The model is applied to birth defects data, where continuous data on the size of infants who were exposed to anticonvulsant medications in utero are compared to controls. Copyright © 1999 John Wiley & Sons, Ltd.