ArticleAnnals of epidemiology2026
Multilevel modeling in egocentric network analysis: A practical guide with SAS and R.
Article in Annals of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis paper illustrates the application of multilevel modeling to egocentric network data, where network alters are nested within their respective egos. The nested structure and intra-ego dependencies in such data violate the independence assumptions of traditional regression models.
methodsMultilevel modeling addresses this dependency by accommodating hierarchical data structures, allowing for more accurate estimation of ego-alter associations. It also distinguishes the effects of variables measured at the alter, ego, and dyadic (ego-alter) levels on outcome variable. We describe model specifications involving random intercepts and slopes, cross-level interactions, and assumptions related to residuals and variance structures. An illustrative example is provided to demonstrate how to estimate fixed and random effects for both continuous and binary outcome variables, assess intraclass correlation, test cross-level interactions, and interpret model results.
resultsThis paper serves as a practical guide for applying multilevel models to egocentric network data, outlining key conceptual foundations, methodological considerations, and step-by-step implementation using SAS and R.
conclusionsThe guide aims to support researchers in the social and health sciences in rigorously applying multilevel modeling to egocentric network data, fostering deeper insights into how individual, relational, and structural factors influence health-related outcomes.
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