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Canadian Journal of Public Health

, Volume 92, Issue 2, pp 150–154 | Cite as

An Introduction to Multilevel Regression Models

  • Peter C. AustinEmail author
  • Vivek Goel
  • Carl van Walraven
Article
  • 3 Downloads

Abstract

Data in health research are frequently structured hierarchically. For example, data may consist of patients nested within physicians, who in turn may be nested in hospitals or geographic regions. Fitting regression models that ignore the hierarchical structure of the data can lead to false inferences being drawn from the data. Implementing a statistical analysis that takes into account the hierarchical structure of the data requires special methodologies.

In this paper, we introduce the concept of hierarchically structured data, and present an introduction to hierarchical regression models. We then compare the performance of a traditional regression model with that of a hierarchical regression model on a dataset relating test utilization at the annual health exam with patient and physician characteristics. In comparing the resultant models, we see that false inferences can be drawn by ignoring the structure of the data.

Résumé

Dans le domaine de la recherche en santé, les données sont souvent structurées de façon hiérarchique. Par exemple, des données peuvent regrouper des patients reliés à des médecins, qui à leur tour sont reliés à un hôpital ou une région géographique. L’élaboration de modèles de régression qui négligent cette structure hiérarchique peut mener à des conclusions erronées. La réalisation d’une analyse statistique qui tient compte de la hiérarchie des données requiert des méthodes spécifiques.

Dans notre article, nous présentons le concept des structures hiérarchisées de données et initions le lecteur aux modèles de régression hiérarchiques. Nous comparons ensuite les résultats d’un modèle de régression traditionnel à ceux d’un modèle hiérarchique appliqué à un fichier qui établit des liens entre l’utilisation de tests lors d’examens annuels de santé et les caractéristiques des patients et des médecins en cause. La comparaison entre les deux modèles montre que l’on peut tirer de fausses conclusions si l’on ne tient pas compte de la structure des données.

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Copyright information

© The Canadian Public Health Association 2001

Authors and Affiliations

  • Peter C. Austin
    • 1
    • 4
    Email author
  • Vivek Goel
    • 2
  • Carl van Walraven
    • 3
    • 5
    • 6
  1. 1.Institute for Clinical Evaluative Sciences (ICES)University of TorontoNorth YorkCanada
  2. 2.Departments of Health Administration and Public Health SciencesUniversity of TorontoCanada
  3. 3.University of OttawaCanada
  4. 4.Department of Public Health SciencesUniversity of TorontoTorontoCanada
  5. 5.Clinical Epidemiology UnitCanada
  6. 6.ICESCanada

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