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Cited by 300

The DOI matches its Crossref record.

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

Begg, Melissa D., Parides, Michael K.·2003·Statistics in Medicine
DOI 10.1002/sim.1524
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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. Copyright © 2003 John Wiley & Sons, Ltd.

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Authors
Begg, M. D., & Parides, M. K.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2003-07-23
Volume
22
Issue
16
Pages
2591-2602
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.1524
OpenAlex
W2002256171
Cited by
300
References
0
FWCI
5.22
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.