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AJPH First Look, published online ahead of print Jun 12, 2008
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AJPH.2006.108233v1
98/8/1425    most recent
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August 2008, Vol 98, No. 8 | American Journal of Public Health 1425-1429
© 2008 American Public Health Association
DOI: 10.2105/AJPH.2006.108233

INNOVATIONS IN DESIGN AND ANALYSIS

A Comparison of Regression Approaches for Analyzing Clustered Data

Manisha Desai, PhD and Melissa D. Begg, ScD

Manisha Desai and Melissa D. Begg are with the Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY.

Correspondence: Requests for reprints should be sent to Manisha Desai, PhD, Department of Biostatistics, Columbia University, 722 West 168th St, R646, New York, NY 10032 (e-mail: manisha.desai{at}columbia.edu).

ABSTRACT

Objectives. We used 3 approaches to analyzing clustered data to assess the impact of model choice on interpretation.

Methods. Approaches 1 and 2 specified random intercept models but differed in standard versus novel specification of covariates, which impacts ability to separate within- and between-cluster effects. Approach 3 was based on standard analysis of paired differences. We applied these methods to data from the National Collaborative Perinatal Project to examine the association between head circumference at birth and intelligence (IQ) at age 7 years.

Results. Approach 1, which ignored within- and between-family effects, yielded an overall IQ effect of 1.1 points (95% confidence interval [CI]=0.9, 1.3) for every 1-cm increase in head circumference. Approaches 2 and 3 found comparable within-family effects of 0.6 points (95% CI = 0.4, 0.9) and 0.69 points (95% CI = 0.4, 1.0), respectively.

Conclusions. Our findings confirm the importance of applying appropriate analytic methods to clustered data, as well as the need for careful covariate specification in regression modeling. Method choice should be informed by the level of interest in cluster-level effects and item-level effects.






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