Disease occurrence, clinical manifestations, and outcomes differ between men and women. Yet, women and men are most of the time treated similarly, which is often based on experimental data over-representing one sex. Accounting for persisting sex bias in biomedical research is the misconception that the analysis of sex-specific effects would double sample size and costs. We designed an analysis to test the potential benefits of a factorial study design in the context of a study including male and female animals. We chose a 2 × 2 factorial design approach to study the effect of treatment, sex, and an interaction term of treatment and sex in a hypothetical situation. We calculated the sample sizes required to detect an effect of a given magnitude with sufficient power and under different experimental setups. We demonstrated that the inclusion of both sexes in experimental setups, without testing for sex effects, requires no or few additional animals in our scenarios. These experimental designs still allow for the exploration of sex effects at low cost. In a confirmatory instead of an exploratory design, we observed an increase in total sample sizes by 33%, at most. Since the complexities associated with this mathematical model require statistical expertise, we generated and provide a sample size calculator for planning factorial design experiments. For the inclusion of sex, a factorial design is advisable, and a sex-specific analysis can be performed without excessive additional effort. Our easy-to-use calculation tool provides help in designing studies with both sexes and addresses the current sex bias in preclinical studies.
• Both sexes should be included into animal studies.
• Exploratory study of sex effects can be conducted with no or small increase in animal number.
• Confirmatory analysis of sex effects requires maximum 33% more animals per study.
• Our calculation tool supports the design of studies with both sexes.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Wizeman TM, Pardue ML (2001) Exploring the biological contributions to human health: does sex matter? In: National Academy Press. USA, Washington, DC
Itoh Y, Arnold AP (2015) Are females more variable than males in gene expression? Meta-analysis of microarray datasets. Biol Sex Differ 6:18
Dayton A, Exner EC, Bukowy JD, Stodola TJ, Kurth T, Skelton M, Greene AS, Cowley AW Jr (2016) Breaking the cycle: estrous variation does not require increased sample size in the study of female rats. Hypertension (Dallas, Tex : 1979) 68(5):1139–1144
Beery AK (2018) Inclusion of females does not increase variability in rodent research studies. Curr Opin Behav Sci 23:143–149
Regitz-Zagrosek V (2014) Sex and gender differences in pharmacotherapy. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 57(9):1067–1073
Beery AK, Zucker I (2011) Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 35(3):565–572
New supplemental awards apply sex and gender lens to NIH-funded research. (2014). https://www.nih.gov/news-events/news-releases/new-supplemental-awards-apply-sex-gender-lens-nih-funded-research. Accessed 10.06.2018 2018
Clayton JA, Collins FS (2014) Policy: NIH to balance sex in cell and animal studies. Nature 509(7500):282–283
Research CIoH (2018) How to integrate sex and gender into research. http://www.cihr-irsc.gc.ca/e/50836.html
Guidance on Gender Equality in Horizon 2020 (2016). Version 2 edn.,
Committee GP (2018) Gender Policy Committee. The European Association of Science Editors (EASE). http://www.ease.org.uk/strategy-groups/gender-policy-committee/. Accessed 11.06.2018 2018
Heidari S, Babor TF, De Castro P, Tort S, Curno M (2016) Sex and gender equity in research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev 1:2
Bryant J, Yi P, Miller L, Peek K, Lee D (2018) Potential sex Bias exists in orthopaedic basic science and translational research. J Bone Joint Surg Am 100(2):124–130
Florez-Vargas O, Brass A, Karystianis G, Bramhall M, Stevens R, Cruickshank S, Nenadic G (2016) Bias in the reporting of sex and age in biomedical research on mouse models. eLife 5. https://doi.org/10.7554/eLife.13615
Potluri T, Engle K, Fink AL, Vom Steeg LG, Klein SL (2017) Sex reporting in preclinical microbiological and immunological research. mBio 8(6). https://doi.org/10.1128/mBio.01868-17
Ramirez FD, Motazedian P, Jung RG, Di Santo P, MacDonald ZD, Moreland R, Simard T, Clancy AA, Russo JJ, Welch VA, Wells GA, Hibbert B (2017) Methodological rigor in preclinical cardiovascular studies: targets to enhance reproducibility and promote research translation. Circ Res 120(12):1916–1926
Stephenson ED, Farzal Z, Kilpatrick LA, Senior BA, Zanation AM (2018) Sex bias in basic science and translational otolaryngology research. Laryngoscope. https://doi.org/10.1002/lary.27498
Will TR, Proano SB, Thomas AM, Kunz LM, Thompson KC, Ginnari LA, Jones CH, Lucas SC, Reavis EM, Dorris DM, Meitzen J (2017) Problems and progress regarding sex Bias and omission in neuroscience research. eNeuro 4(6). https://doi.org/10.1523/eneuro.0278-17.2017
Yoon DY, Mansukhani NA, Stubbs VC, Helenowski IB, Woodruff TK, Kibbe MR (2014) Sex bias exists in basic science and translational surgical research. Surgery 156(3):508–516
Russell WMS, Burch RL, Hume CW (1959) The principles of humane experimental technique
Fisher RA (1935) The design of experiments. Oliver and Boyd
Festing MF (1994) Reduction of animal use: experimental design and quality of experiments. Lab Anim 28(3):212–221
Festing MF (1992) The scope for improving the design of laboratory animal experiments. Lab Anim 26(4):256–268
Miller LR, Marks C, Becker JB, Hurn PD, Chen WJ, Woodruff T, McCarthy MM, Sohrabji F, Schiebinger L, Wetherington CL, Makris S, Arnold AP, Einstein G, Miller VM, Sandberg K, Maier S, Cornelison TL, Clayton JA (2017) Considering sex as a biological variable in preclinical research. FASEB J 31(1):29–34
Montgomery DC (2012) Design and analysis of experiments. Wiley, Hoboken
Faul F, Erdfelder E, Buchner A, Lang AG (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41(4):1149–1160
Champely S, Ekstrom C, Dalgaard P, Gill J, Weibelzahl S, Anandkumar A, Ford C, Volcic R, HDe Rosario H (2018) Basic functions for power analysis. https://github.com/heliosdrm/pwr. Accessed 11.06.2018
Fan FY (2017) Basic functions for power analysis and effect size calculation. https://cran.r-project.org/web/packages/powerAnalysis/index.html. Accessed 11.06.2018 2018
StatTools : Resource Index (Subjects). (2014) Chinese University of Hongkong: Department of Obstretics and Gynaecology. http://www.obg.cuhk.edu.hk/ResearchSupport/StatTools/ResourceIndex_Subjects.php. Accessed 11.06.2018 2018
Bioinformatics Q (2018) Power or sample size calculator. https://www.anzmtg.org/stats/PowerCalculator. Accessed 11.06.2018 2018
Zaiontz C (2018) Real statistics using Excel. www.real-statistics.com. Accessed 11.06.2018 2018
TB was supported by the Hertie Foundation and the Swiss Multiple Sclerosis Society. CG was supported by grants from the Swiss National Science Foundation, the Olga Mayenfisch Foundation, Switzerland, the OPO Foundation, Switzerland, the Novartis Foundation, Switzerland, the Swissheart Foundation, and the Helmut Horten Foundation, Switzerland. KM was supported by the German Research Foundation (DFG).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Buch, T., Moos, K., Ferreira, F.M. et al. Benefits of a factorial design focusing on inclusion of female and male animals in one experiment. J Mol Med 97, 871–877 (2019). https://doi.org/10.1007/s00109-019-01774-0
- Animal experimentation
- Factorial design