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

  • Authors:

  • James M. McMahon;Enrique R. Pouget;Stephanie Tortu

  • Affiliations:

  • National Development and Research Institutes, 71 West 23rd Street, 8th fl., New York, New York 10010, USA;National Development and Research Institutes, 71 West 23rd Street, 8th fl., New York, New York 10010, USA and Yale University, School of Epidemiology and Public Health, New Haven, CT, USA;Louisiana State University, School of Public Health, New Orleans, LA, USA

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2006

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Abstract

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.