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The Need for Randomization in Animal Trials: An Overview of Systematic Reviews
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BACKGROUND AND OBJECTIVES: Randomization, allocation concealment, and blind outcome assessment have been shown to reduce bias in human studies. Authors from the Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Studies (CAMARADES) collaboration recently found that these features protect against bias in animal stroke studies. We extended the scope the work from CAMARADES to include investigations of treatments for any condition. METHODS: We conducted an overview of systematic reviews. We searched Medline and Embase for systematic reviews of animal studies testing any intervention (against any control) and we included any disease area and outcome. We included reviews comparing randomized versus not randomized (but otherwise controlled), concealed versus unconcealed treatment allocation, or blinded versus unblinded outcome assessment. RESULTS: Thirty-one systematic reviews met our inclusion criteria: 20 investigated treatments for experimental stroke, 4 reviews investigated treatments for spinal cord diseases, while 1 review each investigated treatments for bone cancer, intracerebral hemorrhage, glioma, multiple sclerosis, Parkinson's disease, and treatments used in emergency medicine. In our sample 29% of studies reported randomization, 15% of studies reported allocation concealment, and 35% of studies reported blinded outcome assessment. We pooled the results in a meta-analysis, and in our primary analysis found that failure to randomize significantly increased effect sizes, whereas allocation concealment and blinding did not. In our secondary analyses we found that randomization, allocation concealment, and blinding reduced effect sizes, especially where outcomes were subjective. CONCLUSIONS: Our study demonstrates the need for randomization, allocation concealment, and blind outcome assessment in animal research across a wide range of outcomes and disease areas. Since human studies are often justified based on results from animal studies, our results suggest that unduly biased animal studies should not be allowed to constitute part of the rationale for human trials.
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“There are, however, compelling data from systematic reviews showing that nonblinded outcome assessment leads to the treatment effects being overestimated, and the lack of bias-reducing measures such as randomisation and blinding can contribute to as much as 30%–45% inflation of effect sizes [67,68,85].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [67]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“There are, however, compelling data from systematic reviews showing that nonblinded outcome assessment leads to the treatment effects being overestimated, and the lack of bias-reducing measures such as randomisation and blinding can contribute to as much as 30%–45% inflation of effect sizes [67,68,85].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [67]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding are more likely to report exaggerated effects that meet conventional measures of statistical significance [67–69].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [67]
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Metadata
- Authors
- Hirst, J. A., Howick, J., Aronson, J. K., Roberts, N., Perera, R., Koshiaris, C., & Heneghan, C.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Hirst, Jennifer A.
- Howick, Jeremy
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-06-06
- Volume
- 9
- Issue
- 6
- Pages
- e98856
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4048216
- OpenAlex
- W2037669635
- Cited by
- 294
- References
- 69
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 20 · 2023: 25 · 2022: 28 · 2021: 38 · 2020: 30 · 2019: 21 · 2018: 20 · 2017: 19 · 2016: 32 · 2015: 16 · 2014: 1
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- randomization, allocation concealment, blinding, animal models, effect size
- MeSH terms
- Animals, Research Design, Review Literature as Topic, Bias, Animal Experimentation
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