Comparison of methods for analyzing binary data arising from two-sample twin studies

The study of twins is widely used for research into genetic and environmental influences on human outcome measurements. For the study design in which independent samples of monozygotic and dizygotic twins are compared with respect to their similarity on a binary trait, several statistical methods have been proposed. Using a Monte Carlo simulation, we compare the five following procedures: 1) goodness-of-fit method based on the common correlation model, 2) normal approximation of the maximum likelihood estimators of the common correlation coefficients, 3) Ramakrishnan et al. [(1992) Genet Epidemiol 9:273-282] method of odds ratio comparison, 4) generalized estimating equations method of odds ratio estimation, and 5) tetrachoric correlation method. The results show that the goodness-of-fit approach has similar or better performance in both type-one error rates and power than the other methods in all parameter settings. Its advantage with respect to type-one error rates is particularly clear under conditions of small sample sizes, extreme prevalences, or high values of the intraclass correlation coefficients. Therefore, the goodness-of-fit method is recommended for the two-sample twin study design.