Benefits of a factorial design focusing on inclusion of female and male animals in one experiment


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.

Key messages

• 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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Wizeman TM, Pardue ML (2001) Exploring the biological contributions to human health: does sex matter? In: National Academy Press. USA, Washington, DC

    Google Scholar 

  2. 2.

    Itoh Y, Arnold AP (2015) Are females more variable than males in gene expression? Meta-analysis of microarray datasets. Biol Sex Differ 6:18

    Article  Google Scholar 

  3. 3.

    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

    CAS  Article  Google Scholar 

  4. 4.

    Beery AK (2018) Inclusion of females does not increase variability in rodent research studies. Curr Opin Behav Sci 23:143–149

    Article  Google Scholar 

  5. 5.

    Regitz-Zagrosek V (2014) Sex and gender differences in pharmacotherapy. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 57(9):1067–1073

    CAS  Article  Google Scholar 

  6. 6.

    Beery AK, Zucker I (2011) Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 35(3):565–572

    Article  Google Scholar 

  7. 7.

    New supplemental awards apply sex and gender lens to NIH-funded research. (2014). Accessed 10.06.2018 2018

  8. 8.

    Clayton JA, Collins FS (2014) Policy: NIH to balance sex in cell and animal studies. Nature 509(7500):282–283

    Article  Google Scholar 

  9. 9.

    Research CIoH (2018) How to integrate sex and gender into research.

    Google Scholar 

  10. 10.

    Guidance on Gender Equality in Horizon 2020 (2016). Version 2 edn.,

    Google Scholar 

  11. 11.

    Committee GP (2018) Gender Policy Committee. The European Association of Science Editors (EASE). Accessed 11.06.2018 2018

  12. 12.

    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

    Article  Google Scholar 

  13. 13.

    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

    Article  Google Scholar 

  14. 14.

    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.

  15. 15.

    Potluri T, Engle K, Fink AL, Vom Steeg LG, Klein SL (2017) Sex reporting in preclinical microbiological and immunological research. mBio 8(6).

  16. 16.

    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

    CAS  Article  Google Scholar 

  17. 17.

    Stephenson ED, Farzal Z, Kilpatrick LA, Senior BA, Zanation AM (2018) Sex bias in basic science and translational otolaryngology research. Laryngoscope.

    Article  Google Scholar 

  18. 18.

    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).

    Article  Google Scholar 

  19. 19.

    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

    Article  Google Scholar 

  20. 20.

    Russell WMS, Burch RL, Hume CW (1959) The principles of humane experimental technique

    Google Scholar 

  21. 21.

    Fisher RA (1935) The design of experiments. Oliver and Boyd

  22. 22.

    Festing MF (1994) Reduction of animal use: experimental design and quality of experiments. Lab Anim 28(3):212–221

    CAS  Article  Google Scholar 

  23. 23.

    Festing MF (1992) The scope for improving the design of laboratory animal experiments. Lab Anim 26(4):256–268

    CAS  Article  Google Scholar 

  24. 24.

    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

    CAS  Article  Google Scholar 

  25. 25.

    Montgomery DC (2012) Design and analysis of experiments. Wiley, Hoboken

    Google Scholar 

  26. 26.

    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

    Article  Google Scholar 

  27. 27.

    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. Accessed 11.06.2018

  28. 28.

    Fan FY (2017) Basic functions for power analysis and effect size calculation. Accessed 11.06.2018 2018

  29. 29.

    StatTools : Resource Index (Subjects). (2014) Chinese University of Hongkong: Department of Obstretics and Gynaecology. Accessed 11.06.2018 2018

  30. 30.

    Bioinformatics Q (2018) Power or sample size calculator. Accessed 11.06.2018 2018

  31. 31.

    Zaiontz C (2018) Real statistics using Excel. Accessed 11.06.2018 2018

Download references


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).

Author information




Conceptualization and supervision were performed by TB and AT. Methodology and formal analysis were performed KM. Verification was performed by HF. Visualization was performed by FMF and KM. The original draft was written by TB, FMF, CG, KM, and AT. The manuscript was reviewed and edited by TB, FMF, HF, CG, KM, and AT.

Corresponding authors

Correspondence to Thorsten Buch or Achim Tresch.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(XLSX 2.77 mb)


(DOCX 21 kb)


(R 98 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

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).

Download citation


  • Sex
  • Animal experimentation
  • Factorial design
  • Power