A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED

In the social and health sciences, data are often structured hierarchically, with individuals nested within groups. Dyads constitute a special case of hierarchically structured data with variation at both the individual and dyadic level. Analyses of data from dyads pose several challenges due to the...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 50; no. 12; pp. 3663 - 3680
Main Authors McMahon, James M., Pouget, Enrique R., Tortu, Stephanie
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.08.2006
Elsevier Science
Elsevier
SeriesComputational Statistics & Data Analysis
Subjects
Applications
Binary outcomes
Biology, psychology, social sciences
Dyadic data
Exact sciences and technology
General topics
HLM
Mathematics
Multilevel modeling
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
SAS NLMIXED
Sciences and techniques of general use
Statistics
HLM
Dyadic data
Multilevel modeling
SAS NLMIXED
Binary outcomes
Data analysis
Social sciences
Binary data
Infection
Viral hepatitis
Statistical computation
Viral disease
Analytical method
Binary response
Software
Viral hepatitis C
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Summary:In the social and health sciences, data are often structured hierarchically, with individuals nested within groups. Dyads constitute a special case of hierarchically structured data with variation at both the individual and dyadic level. Analyses of data from dyads pose several challenges due to the interdependence between members within dyads and issues related to small group sizes. Multilevel analytic techniques have been developed and applied to dyadic data in an attempt to resolve these issues. In this article, we describe a set of analyses for modeling individual- and dyad-level influences on binary outcomes using SAS statistical software; and we discuss the benefits and limitations of such an approach. For illustrative purposes, we apply these techniques to estimate individual- and dyad-level predictors of viral hepatitis C infection among heterosexual couples in East Harlem, New York City.
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Corresponding author. Tel.: +1 2128454553; fax: +1 9174380894.E-mail address: mcmahon@ndri.org (J.M. McMahon)
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2005.08.008