Scaled marginal models for multiple continuous outcomes.

Roy, J; Lin, X; Ryan, LM

Scaled marginal models for multiple continuous outcomes.

Roy, J Lin, X Ryan, LM

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Publication Type:: Journal Article
Citation:: Biostatistics (Oxford, England), 2003, 4 (3), pp. 371 - 383
Issue Date:: 2003-01-01

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Field	Value	Language
dc.contributor.author	Roy, J	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Biostatistics (Oxford, England), 2003, 4 (3), pp. 371 - 383	en_US
dc.identifier.issn	1465-4644	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26032
dc.description.abstract	In studies that involve multivariate outcomes it is often of interest to test for a common exposure effect. For example, our research is motivated by a study of neurocognitive performance in a cohort of HIV-infected women. The goal is to determine whether highly active antiretroviral therapy affects different aspects of neurocognitive functioning to the same degree and if so, to test for the treatment effect using a more powerful one-degree-of-freedom global test. Since multivariate continuous outcomes are likely to be measured on different scales, such a common exposure effect has not been well defined. We propose the use of a scaled marginal model for testing and estimating this global effect when the outcomes are all continuous. A key feature of the model is that the effect of exposure is represented by a common effect size and hence has a well-understood, practical interpretation. Estimating equations are proposed to estimate the regression coefficients and the outcome-specific scale parameters, where the correct specification of the within-subject correlation is not required. These estimating equations can be solved by repeatedly calling standard generalized estimating equations software such as SAS PROC GENMOD. To test whether the assumption of a common exposure effect is reasonable, we propose the use of an estimating-equation-based score-type test. We study the asymptotic efficiency loss of the proposed estimators, and show that they generally have high efficiency compared to the maximum likelihood estimators. The proposed method is applied to the HIV data.	en_US
dc.relation.ispartof	Biostatistics (Oxford, England)	en_US
dc.relation.isbasedon	10.1093/biostatistics/4.3.371	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	HIV Infections	en_US
dc.subject.mesh	Treatment Outcome	en_US
dc.subject.mesh	Antiretroviral Therapy, Highly Active	en_US
dc.subject.mesh	Multivariate Analysis	en_US
dc.subject.mesh	Data Interpretation, Statistical	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Cohort Studies	en_US
dc.subject.mesh	Cognition	en_US
dc.subject.mesh	Female	en_US
dc.title	Scaled marginal models for multiple continuous outcomes.	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	4	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0604 Genetics	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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

In studies that involve multivariate outcomes it is often of interest to test for a common exposure effect. For example, our research is motivated by a study of neurocognitive performance in a cohort of HIV-infected women. The goal is to determine whether highly active antiretroviral therapy affects different aspects of neurocognitive functioning to the same degree and if so, to test for the treatment effect using a more powerful one-degree-of-freedom global test. Since multivariate continuous outcomes are likely to be measured on different scales, such a common exposure effect has not been well defined. We propose the use of a scaled marginal model for testing and estimating this global effect when the outcomes are all continuous. A key feature of the model is that the effect of exposure is represented by a common effect size and hence has a well-understood, practical interpretation. Estimating equations are proposed to estimate the regression coefficients and the outcome-specific scale parameters, where the correct specification of the within-subject correlation is not required. These estimating equations can be solved by repeatedly calling standard generalized estimating equations software such as SAS PROC GENMOD. To test whether the assumption of a common exposure effect is reasonable, we propose the use of an estimating-equation-based score-type test. We study the asymptotic efficiency loss of the proposed estimators, and show that they generally have high efficiency compared to the maximum likelihood estimators. The proposed method is applied to the HIV data.

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http://hdl.handle.net/10453/26032