Introducing interaction terms to test group-specific effects overcomes many of the problems associated with running separate analyses by gender. However, these are frequently used incorrectly (Brambor et al. 2006), particularly when employing regression models for binary outcomes common in the biomedical sciences (e.g., the presence or absence of a disease; Allison 1999; Long 2009). Compared to two-way interaction models, three-way interactions are even less well-powered and more difficult to implement and interpret, compounding the problems described above. These issues make it difficult, from a practical standpoint, to examine gender-specific GxE interactions. Given that interaction terms are seldom used at all in the biological or psychological sciences (Patsopoulos et al. 2007), opportunities for interdisciplinary research, cross-fertilization, and replication of these complex effects are currently extremely limited.
