title: Multivariate linear mixed models for multiple outcomes.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA. msammel@cceb.upenn.edu
authors.forename: M
authors.initials: M
authors.lastname: Sammel
authors.forename: X
authors.initials: X
authors.lastname: Lin
authors.forename: L
authors.initials: L
authors.lastname: Ryan
mesh_terms: D000014:Abnormalities, Drug-Induced / Q000453:epidemiology; D000927:Anticonvulsants / Q000009:adverse effects; D015331:Cohort Studies; D000013:Congenital Abnormalities / Q000453:epidemiology / Q000209:etiology*; D003627:Data Interpretation, Statistical; D004827:Epilepsy / Q000150:complications / Q000188:drug therapy; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D016014:Linear Models; D015999:Multivariate Analysis; D011247:Pregnancy; D011248:Pregnancy Complications / Q000188:drug therapy
chemical_list: D000927:Anticonvulsants