Journal of Molecular Medicine

, Volume 97, Issue 6, pp 871–877 | Cite as

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

  • Thorsten BuchEmail author
  • Katharina Moos
  • Filipa M. Ferreira
  • Holger Fröhlich
  • Catherine Gebhard
  • Achim TreschEmail author
Original Article


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.


Sex Animal experimentation Factorial design Power 


Author contribution

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.


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).

Supplementary material

109_2019_1774_MOESM1_ESM.xlsx (2.8 mb)
ESM 1 (XLSX 2.77 mb)
109_2019_1774_MOESM2_ESM.docx (21 kb)
ESM 2 (DOCX 21 kb)


  1. 1.
    Wizeman TM, Pardue ML (2001) Exploring the biological contributions to human health: does sex matter? In: National Academy Press. USA, Washington, DCGoogle 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 6:18CrossRefGoogle 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):1139–1144CrossRefGoogle Scholar
  4. 4.
    Beery AK (2018) Inclusion of females does not increase variability in rodent research studies. Curr Opin Behav Sci 23:143–149CrossRefGoogle Scholar
  5. 5.
    Regitz-Zagrosek V (2014) Sex and gender differences in pharmacotherapy. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 57(9):1067–1073CrossRefGoogle Scholar
  6. 6.
    Beery AK, Zucker I (2011) Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 35(3):565–572CrossRefGoogle 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):282–283CrossRefGoogle 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 1:2CrossRefGoogle 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):124–130CrossRefGoogle 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):1916–1926CrossRefGoogle Scholar
  17. 17.
    Stephenson ED, Farzal Z, Kilpatrick LA, Senior BA, Zanation AM (2018) Sex bias in basic science and translational otolaryngology research. Laryngoscope.
  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).
  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):508–516CrossRefGoogle Scholar
  20. 20.
    Russell WMS, Burch RL, Hume CW (1959) The principles of humane experimental techniqueGoogle Scholar
  21. 21.
    Fisher RA (1935) The design of experiments. Oliver and BoydGoogle Scholar
  22. 22.
    Festing MF (1994) Reduction of animal use: experimental design and quality of experiments. Lab Anim 28(3):212–221CrossRefGoogle Scholar
  23. 23.
    Festing MF (1992) The scope for improving the design of laboratory animal experiments. Lab Anim 26(4):256–268CrossRefGoogle 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):29–34CrossRefGoogle Scholar
  25. 25.
    Montgomery DC (2012) Design and analysis of experiments. Wiley, HobokenGoogle 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):1149–1160CrossRefGoogle 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

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Laboratory Animal ScienceUniversity of ZurichZurichSwitzerland
  2. 2.Institute of Medical Statistics and Computational Biology, Faculty of MedicineUniversity of CologneCologneGermany
  3. 3.Center for Data and Simulation Science (CDS)University of CologneCologneGermany
  4. 4.Bonn-Aachen International Center for IT (b-it)University of BonnBonnGermany
  5. 5.Center for Molecular CardiologyUniversity of ZurichZurichSwitzerland

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