Chapter 9 282 interaction term to main effects). This is a non-trivial difference: the addition of sex as a covariate can remove variation due to the sex, whereas the addition of sex as a discovery variable can reveal sex-specific effects in the study. This difference is also illustrated in Figure 9.6, which shows how different analysis techniques can reveal or disguise sex differences in the effects of a hypothetical treatment. This step is essential to ensure that treatment effects are similar in both sexes: if sex differences in treatment effects are found, researchers could follow up by examining sex- or genderspecific factors, such as reproductive phase or sex hormone use, and only if no sex differences are found, researchers can state that the treatment works for both sexes. Figure 9.6. Illustration of hypothetical study results, analyzed in three different ways. The left panel displays results without accounting for sex, the middle panel displays results after addition of sex as a covariate, and the right panel displays results after addition of sex as an interaction term or after stratified analyses. The right panel shows the only analysis in which the researchers would discover that the treatment only works in females. Created using Biorender.com. There is a large knowledge gap regarding the role of sex and gender in mental health research. This gap has been created and maintained by practices in the scientific community, and the scientific community should collaborate and work together to fill this gap and address the existing inequalities in scientific research. 4.4. Clinical practice integrating sex hormones throughout the lifespan Researchers and clinicians should collaborate to develop better evidencebased treatment pathways for those with hormone-associated mood disorders or symptoms throughout the lifespan. Currently, treatment
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