As more studies begin to address the role of sex as a biological variable (SABV), it has become increasingly important to understand how to collect and analyze the data so that the presence or absence of sex differences can be assessed. This has led to some concerns about how to conduct statistical analyses. In this brief commentary we do not attempt to review the field of sex differences, but provide a conceptual guide to the statistical analysis of sex differences in research with both animal and human subjects.
The use of predominantly male subjects has been documented across myriad research domains—both historically and at present—with the extent of bias varying by subdiscipline [1, 2]. Females have historically been viewed as more variable than males because of the presence of estrous or menstrual cycles in many species, although this belief has been discredited across a wide range of animal models (reviewed in [3, 4]). Noting the potential negative consequences of subject sex bias for women’s health, the National Institutes of Health issued a 1993 act to include women in clinical research, followed by a 2014 policy to encourage balanced use of male and female subjects and tissues by considering SABV during research design and analysis. The mere inclusion of females, however, does not provide insight into the role of sex/gender in physiological, behavioral, and psychological traits, and the majority of studies using male and female subjects fail to report on whether sex differences are present [2, 4].
Types of sex differences
In order to analyze the role of SABV, it is important to understand that there are different types of sex differences, and that these may require different analysis strategies. These categories are not mutually exclusive—more than one type of sex difference may be involved in any given trait. Figure 1 provides a graphical representation of four types of sex differences, as well as guidance on conducting analyses based on the type of sex difference. Specifically, major sex differences that prevent the analysis of males and females on the same scale or metric (Fig. 1a) are qualitative sex differences. Qualitative differences are also present if variables are not related to each other in the same way across the sexes (Fig. 1b). In the case of qualitative sex differences, analyses should be conducted independently by sex and the data reported as though they are two independent experiments. When the average or mean of a dependent variable is different for males and females, these are quantitative sex differences (Fig. 1e) and analyses should be conducted with sex as a factor. There are also sex differences where one aspect of a trait is the same for males and females, but the mechanisms underlying the trait are different, or emerge only under certain conditions: these are latent sex differences (Fig. 1c; also referred to as mechanistic, convergent or divergent). Finally, when the proportions of males and females that exhibit particular traits in response to the independent variable are different, these are population sex differences (Fig. 1d). For latent and population sex differences, the types of analyses will vary based on the data. See the Fig. 1 legend for discussion. For additional examples and discussion of these different types of sex differences refer to . It is important to note that many assessments were developed and validated using exclusively male samples, and when extending these tests to females it is not always clear whether they are operationalizing the same trait in females - a topic which requires further consideration .
How should a researcher approach data analysis in light of potential sex differences of various types? While it is beyond the scope of this brief commentary to give prescriptive guidance for all research involving both males and females, we hope to provide some guiding concepts the reader can use when evaluating their data. First, one should consider what is already known about sex differences in the context of the study topic and consider the data that have been obtained (Fig. 1). For a process that only occurs in one sex or varies fundamentally by sex (i.e., qualitative differences), the analysis must proceed separately by sex or only in the sex in which the process occurs (e.g., specific reproductive functions). When sex should be included as a variable in analyses (i.e., if differences are quantitative) descriptions of the effect size or magnitude of any sex differences may help determine the practical significance of the differences, regardless of their statistical significance, and confirmation of the effect is essential . In yet other cases, sex may not appear to be an important contributor to the final results; in these instances, the lack of sex differences should be documented and reported, and consideration should be given to the ability of the data set to identify sex differences (e.g., statistical power). Specific examples of how such analyses might proceed appear in Table 1. In general, we advocate for reporting as much data as feasible for each sex, analyzed in different ways if appropriate. This avoids selecting data that support a hypothesis or defaulting to the assumption that the differences between the sexes are quantitative. Responses to frequently asked questions concerning the analysis of sex differences also appear in Table 2.
Our goal has been to lay out a strategy for analysis of data collected from both female and male subjects. We emphasize that investigators should assess and consider the types of sex differences present in the data to guide analyses, and to ensure that meaningful results will be obtained.
Funding and disclosures
No funding sources were used in the creation of this commentary. The remaining authors have nothing to disclose.
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These authors contributed equally as co-first authors: Adriene M. Beltz, Annaliese K. Beery
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Beltz, A.M., Beery, A.K. & Becker, J.B. Analysis of sex differences in pre-clinical and clinical data sets. Neuropsychopharmacol. 44, 2155–2158 (2019). https://doi.org/10.1038/s41386-019-0524-3
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