Increasing the statistical power of animal experiments with historical control data


Low statistical power reduces the reliability of animal research; yet, increasing sample sizes to increase statistical power is problematic for both ethical and practical reasons. We present an alternative solution using Bayesian priors based on historical control data, which capitalizes on the observation that control groups in general are expected to be similar to each other. In a simulation study, we show that including data from control groups of previous studies could halve the minimum sample size required to reach the canonical 80% power or increase power when using the same number of animals. We validated the approach on a dataset based on seven independent rodent studies on the cognitive effects of early-life adversity. We present an open-source tool, RePAIR, that can be widely used to apply this approach and increase statistical power, thereby improving the reliability of animal experiments.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Many animal experiments are severely underpowered.
Fig. 2: Historical controls can decrease the number of animals required for sufficiently powered research.
Fig. 3: Sensitivity simulation.

Data availability

The data that support the findings of the current study can be downloaded from

Code availability

All code used in this manuscript is available at


  1. 1.

    Ioannidis, J. P. A. Why most published research findings are false. PLoS Med. 2, e124 (2005).

    Article  Google Scholar 

  2. 2.

    Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Smaldino, P. E. & McElreath, R. The natural selection of bad science. R. Soc. Open Sci. 3, 160384 (2016).

  4. 4.

    Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).

    Article  Google Scholar 

  5. 5.

    Crabbe, J. C., Wahlsten, D. & Dudek, B. C. Genetics of mouse behavior: interactions with laboratory environment. Science 284, 1670–1672 (1999).

    CAS  Article  Google Scholar 

  6. 6.

    Prinz, F., Schlange, T. & Asadullah, K. Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 10, 712–713 (2011).

    CAS  Article  Google Scholar 

  7. 7.

    Voelkl, B. et al. Reproducibility of animal research in light of biological variation. Nat. Rev. Neurosci. 21, 384–393 (2020).

  8. 8.

    Macleod, M. & Mohan, S. Reproducibility and rigor in animal-based research. ILAR J. 60, 17–23 (2019).

    CAS  Article  Google Scholar 

  9. 9.

    Bonapersona, V. et al. The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies. Neurosci. Biobehav. Rev. 102, 299–307 (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Richter, S. H., Garner, J. P. & Würbel, H. Environmental standardization: cure or cause of poor reproducibility in animal experiments? Nat. Methods 6, 257–261 (2009).

    CAS  Article  Google Scholar 

  12. 12.

    Karp, N. A. Reproducible preclinical research—is embracing variability the answer? PLoS Biol. 16, e2005413 (2018).

    Article  Google Scholar 

  13. 13.

    Richter, S. H. et al. Effect of population heterogenization on the reproducibility of mouse behavior: a multi-laboratory study. PLoS ONE 6, e16461 (2011).

  14. 14.

    Kramer, M. & Font, E. Reducing sample size in experiments with animals: historical controls and related strategies. Biol. Rev. Philos. Soc. 92, 431–445 (2017).

    Article  Google Scholar 

  15. 15.

    Brakenhoff, T., Roes, K. & Nikolakopoulos, S. Bayesian sample size re-estimation using power priors. Stat. Methods Med. Res. 28, 1664–1675 (2018).

  16. 16.

    Spiegelhalter, D. J., Abrams, K. R. & Myles, J. P. Bayesian Approaches to Clinical Trials and Health-Care Evaluation 1 (Wiley, 2004).

  17. 17.

    Galwey, N. W. Supplementation of a clinical trial by historical control data: is the prospect of dynamic borrowing an illusion? Stat. Med. 36, 899–916 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Mutsvari, T., Tytgat, D. & Walley, R. Addressing potential prior-data conflict when using informative priors in proof-of-concept studies. Pharm. Stat. 15, 28–36 (2016).

    Article  Google Scholar 

  19. 19.

    Walley, R. et al. Using Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals. Pharm. Stat. 15, 277–285 (2016).

    Article  Google Scholar 

  20. 20.

    Nord, C. L., Valton, V., Wood, J. & Roiser, J. P. Power-up: a reanalysis of ‘power failure’ in neuroscience using mixture modeling. J. Neurosci. 37, 8051–8061 (2017).

    CAS  Article  Google Scholar 

  21. 21.

    Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, 1977).

  22. 22.

    Ibrahim, J. G. & Chen, M. H. Power prior distributions for regression models. Stat. Sci. 15, 46–60 (2000).

    Article  Google Scholar 

  23. 23.

    Rice, C. J., Sandman, C. A., Lenjavi, M. R. & Baram, T. Z. A novel mouse model for acute and long-lasting consequences of early life stress. Endocrinology 149, 4892–4900 (2008).

    CAS  Article  Google Scholar 

  24. 24.

    Percie du Sert, N. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020).

    CAS  Article  Google Scholar 

  25. 25.

    Ioannidis, J. P. A. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008).

    Article  Google Scholar 

  26. 26.

    Rubin, E. J. & Fortune, S. M. Misunderstanding the goals of animal research. BMJ 360, 29321149 (2018).

    Google Scholar 

  27. 27.

    Gelman, A. Objections to Bayesian statistics. Bayesian Anal. 3, 445–450 (2008).

    Article  Google Scholar 

  28. 28.

    Viele, K. et al. Use of historical control data for assessing treatment effects in clinical trials. Pharm. Stat. 13, 41–54 (2014).

    Article  Google Scholar 

  29. 29.

    Neuenschwander, B., Capkun-Niggli, G., Branson, M. & Spiegelhalter, D. J. Summarizing historical information on controls in clinical trials. Clin. Trials 7, 5–18 (2010).

    Article  Google Scholar 

  30. 30.

    O’Hagan, A. Expert knowledge elicitation: subjective but scientific. Am. Stat. 73, 69–81 (2019).

    Article  Google Scholar 

  31. 31.

    van der Naald, M., Wenker, S., Doevendans, P. A., Wever, K. E. & Chamuleau, S. A. J. Publication rate in preclinical research: a plea for preregistration. BMJ Open Sci. 4, e100051 (2020).

    Article  Google Scholar 

  32. 32.

    Du Sert, N. P. et al. The experimental design assistant. Nat. Methods 14, 1024–1025 (2017).

    Article  Google Scholar 

  33. 33.

    Crabbe, J. C. & Phillips, T. J. Mother nature meets mother nurture. Nat. Neurosci. 6, 440–442 (2003).

    CAS  Article  Google Scholar 

  34. 34.

    Kafkafi, N. et al. Addressing reproducibility in single-laboratory phenotyping experiments. Nat. Methods 14, 462–464 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Shansky, R. M. Are hormones a ‘female problem’ for animal research? Science 364, 825–826 (2019).

    CAS  Article  Google Scholar 

  36. 36.

    Bonapersona, V., Joëls, M. & Sarabdjitsingh, R. A. Effects of early life stress on biochemical indicators of the dopaminergic system: a 3 level meta-analysis of rodent studies. Neurosci. Biobehav. Rev. 95, 1–16 (2018).

    CAS  Article  Google Scholar 

  37. 37.

    Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  Google Scholar 

  38. 38.

    Chang, W., Cheng, J., Allaire, J., Xie, Y. & McPherson, J. shiny: Web Application Framework for R. R Package version 1.5.0 (2020).

  39. 39.

    Ekstrøm, C. T. MESS: Miscellaneous Esoteric Statistical Scripts. R package version 0.5.6 (2019).

  40. 40.

    Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).

    Article  Google Scholar 

  41. 41.

    Lenth, R. V. Some practical guidelines for effective sample size determination. Am. Stat. 55, 187–193 (2001).

    Article  Google Scholar 

  42. 42.

    Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis. (Chapman & Hall, 1995).

Download references


We thank J. Knop and M. Sep for helpful discussions and R. de Kloet for critically reviewing the manuscript. This work was supported by the Consortium of Individual Development (CID), which is funded by the Gravitation program of the Dutch Ministry of Education, Culture and Science and the Netherlands Organization for Scientific Research (NWO grant no. 024.001.003).

Author information





V.B. contributed to conceptualization, data curation, analysis, investigation, methodology, software, visualization and writing the manuscript; H.H. contributed to conceptualization, analysis, methodology, supervision and reviewing and editing the manuscript; members of the RELACS consortium provided the data; R.A.S. contributed to conceptualization, project administration, supervision and editing and reviewing the manuscript; M.J. contributed to conceptualization, funding acquisition, project administration, supervision and writing the manuscript.

Corresponding author

Correspondence to V. Bonapersona.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Stanley Lazic, Malcolm MacLeod, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Notes 1–4, Supplementary Fig. 1 and Supplementary Tables 1–4

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bonapersona, V., Hoijtink, H., RELACS Consortium. et al. Increasing the statistical power of animal experiments with historical control data. Nat Neurosci (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing