Analyzing family data: A GEE approach for substance use researchers

Analyzing data that arises from correlated observations such as husband-wife pairs, siblings, or repeated assessments of the same individuals over time requires more specialized analytic tools. Additionally, outcomes that are not normally distributed such as count data, (e.g., number of symptoms or number of problems endorsed) also require specialized analytic tools. Generalized estimating equations (GEE) are a very flexible tool for dealing with correlated data (such as data derived from related individuals such as families). The objective of this report was to compare traditional ordinary least squares regression (OLS) to a GEE approach for analyzing family data.

Using data from an ongoing five-wave longitudinal study of newlywed couples, we examined a subset of 173 families with children between the ages of 4 and 11 at two data collection points. The relation between parental risk factors (e.g., heavy drinking, aggression, marital quality) and child internalizing symptoms was examined within the context of two regression-based models: traditional OLS regression and a GEE approach.

Overall, the GEE approach allowed a more complete use of the available data, provided more robust findings, and produced more reliable parameter estimates.

GEE models are a flexible regression-based approach for dealing with related data that arises from correlated data such as family data. Further, given the availability of the models in common statistical programs, family researchers should consider these models for their work.