Abstract: In family studies, unbalanced family size and complicated pedigree structures pose challenges in the data analysis. Although the generalized estimating equations (GEE) have been commonly used, there is no systematic study to evaluate the GEE performance on family data. To close the knowledge gap, we performed a simulation study for family data with continuous, binary and frequency data type. Kinship matrix, derived by the SHFS Genetic Center, was used to estimate the correlation within each family. The SHFS data included 2,964 individuals from 91 families. The size of family ranges from 1 to 113. Coverage probability and relative bias were used to evaluate the GEE performance. Our simulation results showed that GEE with independent correlation structure worked well for continuous data. However, if the assumption of a generalized linear mixed effect model (GLMM) holds true, GEE did not work as well for the frequency and binary data as it produced biased parameter estimates and coverage below the nominal level. We recommend a model selection procedure such as cross-validation should be carried out to determine whether a GEE or a GLMM is a better fit for the data.