A comparison of regression approaches for analyzing clustered data

Area of Science:

• Biostatistics
• Epidemiology
• Developmental Psychology

Background:

• Clustered data, such as sibling or family data, requires specialized statistical methods for accurate analysis.
• Model selection can influence the interpretation of associations between variables when data are not independent.

Purpose of the Study:

• To evaluate the impact of different analytical approaches on interpreting results from clustered data.
• To compare three distinct methods for analyzing clustered data, focusing on model specification and covariate handling.

Main Methods:

• Three analytical approaches were employed: two random intercept models with varying covariate specifications and one standard analysis of paired differences.
• The methods were applied to data from the National Collaborative Perinatal Project.
• The study examined the association between head circumference at birth and intelligence (IQ) at age 7.

Main Results:

• An approach ignoring within- and between-family effects estimated an overall IQ increase of 1.1 points per cm increase in head circumference.
• Two other approaches, accounting for within-family effects, found comparable results: 0.6 points (95% CI = 0.4, 0.9) and 0.69 points (95% CI = 0.4, 1.0).

Conclusions:

• Appropriate analytical methods are essential for correctly interpreting clustered data.
• Careful covariate specification in regression modeling is critical for distinguishing between within- and between-cluster effects.
• The choice of statistical method should align with research interest in cluster-level versus item-level effects.