Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data

Melissa D. Begg, Michael K. Parides

Research output: Contribution to journalReview article

196 Scopus citations

Abstract

The focus of this paper is regression analysis of clustered data. Although the presence of intracluster correlation (the tendency for items within a cluster to respond alike) is typically viewed as an obstacle to good inference, the complex structure of clustered data offers significant analytic advantages over independent data. One key advantage is the ability to separate effects at the individual (or item-specific) level and the group (or cluster-specific) level. We review different approaches for the separation of individual-level and cluster-level effects on response, their appropriate interpretation and give recommendations for model fitting based on the intent of the data analyst. Unlike many earlier papers on this topic, we place particular emphasis on the interpretation of the cluster-level covariate effect. The main ideas of the paper are highlighted in an analysis of the relationship between birth weight and IQ using sibling data from a large birth cohort study.

Original languageEnglish (US)
Pages (from-to)2591-2602
Number of pages12
JournalStatistics in Medicine
Volume22
Issue number16
DOIs
StatePublished - Aug 30 2003
Externally publishedYes

Keywords

  • Between-cluster effects
  • Clustered data analysis
  • Covariate selection
  • Model misspecification
  • Within-cluster effects

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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