Abstract
As part of an initiative to improve rigor and reproducibility in biomedical research, the U.S. National Institutes of Health now requires the consideration of sex as a biological variable in preclinical studies. This new policy has been interpreted by some as a call to compare males and females with each other. Researchers testing for sex differences may not be trained to do so, however, increasing risk for misinterpretation of results. Using a list of recently published articles curated by Woitowich et al. (eLife, 2020; 9:e56344), we examined reports of sex differences and non-differences across nine biological disciplines. Sex differences were claimed in the majority of the 147 articles we analyzed; however, statistical evidence supporting those differences was often missing. For example, when a sex-specific effect of a manipulation was claimed, authors usually had not tested statistically whether females and males responded differently. Thus, sex-specific effects may be over-reported. In contrast, we also encountered practices that could mask sex differences, such as pooling the sexes without first testing for a difference. Our findings support the need for continuing efforts to train researchers how to test for and report sex differences in order to promote rigor and reproducibility in biomedical research.
Biomedical research has a long history of including only men or male laboratory animals in studies. To address this disparity, the United States National Institutes of Health (NIH) rolled out a policy in 2016 called Sex as a Biological Variable (or SABV). The policy requires researchers funded by the NIH to include males and females in every experiment unless there is a strong justification not to, such as studies of ovarian cancer. Since then, the number of research papers including both sexes has continued to grow.
Although the NIH does not require investigators to compare males and females, many researchers have interpreted the SABV policy as a call to do so. This has led to reports of sex differences that would otherwise have been unrecognized or ignored. However, researchers may not be trained on how best to test for sex differences in their data, and if the data are not analyzed appropriately this may lead to misleading interpretations.
Here, Garcia-Sifuentes and Maney have examined the methods of 147 papers published in 2019 that included both males and females. They discovered that more than half of these studies had reported sex differences, but these claims were not always backed by statistical evidence. Indeed, in a large majority (more than 70%) of the papers describing differences in how males and females responded to a treatment, the impact of the treatment was not actually statistically compared between the sexes. This suggests that sex-specific effects may be over-reported. In contrast, Garcia-Sifuentes and Maney also encountered instances where an effect may have been masked due to data from males and females being pooled together without testing for a difference first.
These findings reveal how easy it is to draw misleading conclusions from sex-based data. Garcia-Sifuentes and Maney hope their work raises awareness of this issue and encourages the development of more training materials for researchers.
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