The FHS presented additional challenges beyond that of multiple comparisons and measurement of environmental exposures when testing for G×E interactions. The longitudinal, family-based design resulted in data that were correlated in two ways: repeated measurements taken in the same individual at multiple time points and measurements taken in members of the same family. Shi et al. [2009] addressed both types of correlation by applying a three-level hierarchical linear mixed-effects model to account for correlation due to the family-based longitudinal data. Using the simulated data, they found this model to be generally more powerful than using a cross-sectional model that accounted for familial correlation. Joubert et al. [2009] used a novel variance-component method to account for both repeated measures and familial correlation. Maenner et al. [2009] utilized the longitudinal data by analyzing age at onset of CHD as the outcome. Gu et al. [2009] used a two-level factor analysis for longitudinal data. Maenner et al. and Gu et al. used a generalized estimating equations model to confirm the results from their primary analysis while accounting for familial correlation. The use of
