A comparison of regression approaches for analyzing clustered data.

Abstract

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