Multivariate linear mixed models for multiple outcomes

Sammel, M; Lin, X; Ryan, L

Multivariate linear mixed models for multiple outcomes

Sammel, M Lin, X Ryan, L

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Publication Type:: Journal Article
Citation:: Statistics in Medicine, 1999, 18 (17-18), pp. 2479 - 2492
Issue Date:: 1999-09-30

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Field	Value	Language
dc.contributor.author	Sammel, M	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Ryan, L https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	1999-09-30	en_US
dc.identifier.citation	Statistics in Medicine, 1999, 18 (17-18), pp. 2479 - 2492	en_US
dc.identifier.issn	0277-6715	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26223
dc.description.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.	en_US
dc.relation.ispartof	Statistics in Medicine	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Epilepsy	en_US
dc.subject.mesh	Pregnancy Complications	en_US
dc.subject.mesh	Abnormalities, Drug-Induced	en_US
dc.subject.mesh	Anticonvulsants	en_US
dc.subject.mesh	Multivariate Analysis	en_US
dc.subject.mesh	Data Interpretation, Statistical	en_US
dc.subject.mesh	Factor Analysis, Statistical	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Cohort Studies	en_US
dc.subject.mesh	Pregnancy	en_US
dc.subject.mesh	Infant, Newborn	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Congenital Abnormalities	en_US
dc.title	Multivariate linear mixed models for multiple outcomes	en_US
dc.type	Journal Article
utslib.citation.volume	17-18	en_US
utslib.citation.volume	18	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	17-18	en_US
pubs.publication-status	Published	en_US
pubs.volume	18	en_US

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26223