Inclusion of females does not increase variability in rodent research studies

Abstract

The underrepresentation of female subjects in animal research has gained attention in recent years, and new NIH guidelines aim to address this problem early, at the grant proposal stage. Many researchers believe that use of females will hamper research because of the need for increased sample sizes, and increased costs. Here I review empirical research across multiple rodent species and traits that demonstrates that females are not more variable than males, and that for most traits, female estrous cyclicity need not be considered. I present statistical simulations illustrating how factorial designs can reduce the need for additional research subjects, and discuss cultural issues around the inclusion of male and female subjects in research.

Keywords: Sex; female; male; sex differences; sexual dimorphism; variability; variance.

Figure 1.

Data on sex bias and trait variability. A. Inclusion of male and female animal research subjects was surveyed across 10 biological disciplines (neuroscience shown here). Even when both sexes were used, analyses typically did not consider subject sex. Analysis of data from [1]. B. Coefficients of variation were assessed in male and female mice across >9,900 measurements of traits. Variability was similar in males and females, with more male-biased than female-biased traits, and a mean variance ratio significantly lower than 0.5. Modified (with permission) from [22].

Figure 2.

Simulated p-value distributions for different group compositions and treatment effects. Left panels: mean and standard deviation of each group, used to generate 10,000 possible samples of subjects (12f, 12m, or 6f and 6m) in each treatment, according to a gaussian distribution. Coefficients of variation (SD/mean) were constant for each sex/treatment combination so that the spread of groups with different means would be equivalent. Effect sizes for comparisons of treatment A vs. B in all male or female groups were matched (Cohen’s d=1 using SD of lower group, or .97-.99 using pooled SD). Right panels: violin plots of p-values generated for t-tests between different kinds of groups in treatments A vs. B (first three datasets), or from the treatment factor from 2-way ANOVA on mixed sex groups (purple plot). ANOVAs were run with sex and treatment as factors, and an interaction term. The fifth plot (also in purple) represents the distribution of p-values of the ANOVA’s interaction term across runs. Even with a large sex difference, there is no loss from testing half males and half females when a factorial analysis is used, as long as there is no interaction. When an interaction is present, factorial analysis cannot detect a unified treatment effect, but the strong interaction effect will indicate that subgroup analysis by sex and possible follow-up experiments are merited.