Increasing the statistical power of animal experiments with historical control data

RELACS Consortium, V. Bonapersona*, H. Hoijtink, R. A. Sarabdjitsingh, M. Joels

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    31 Citations (Scopus)
    218 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)470-477
    Number of pages11
    JournalNature neuroscience
    Issue number4
    Publication statusPublished - Apr-2021


    Dive into the research topics of 'Increasing the statistical power of animal experiments with historical control data'. Together they form a unique fingerprint.

    Cite this