A call for transparent reporting to optimize the predictive value of preclinical research
Open accessAbstract
Deficiencies in methods reporting in animal experimentation lead to difficulties in reproducing experiments; the authors propose a set of reporting standards to improve scientific communication and study design. Animal studies have contributed immensely to our understanding of diseases and assist the development of new therapies, but inadequate experimental reporting can sometimes render such studies difficult to reproduce and to translate into the clinic. This year, a US National Institute of Neurological Disorders and Stroke workshop addressed this issue, and its conclusions are discussed in a Perspective piece in this issue of Nature. The main workshop recommendation is that at a minimum, studies should report on randomization, blinding, sample-size estimation and how the data were handled. The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.
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In a hypothesis-testing study powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects, but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable.
“In a hypothesis-testing study powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects of the intervention, but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable [88,93].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [93]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredGreater use of measures to control risk of bias in animal experiments, such as randomization and blinding, was correlated with greater effect size decreases in replications.
“In contrast, greater use of measures to control risk of bias in animal experiments, such as randomization and blinding (Landis et al., 2012), was correlated with greater effect size decreases in replications.”
From Estimating the replicability of Brazilian biomedical science (2025) · cites this paper as Landis et al., 2012
Other factors such as blinding, randomization, and sample size planning have been identified as factors that could improve replicability.
“Other factors have been identified that could improve replicability, such as blinding, randomization, and sample size planning (Landis et al., 2012; Macleod and Mohan, 2019).”
From Investigating the replicability of preclinical cancer biology (2021) · cites this paper as Landis et al., 2012
The low replication success rates and small effect sizes we found reinforce prior calls for improving rigor and transparency of preclinical research to improve allocation of limited resources and protect human participants from trials based on weak evidence.
“The low replication success rates and small effect sizes we found reinforce prior calls for improving the rigor and transparency of preclinical research to improve the allocation of limited resources and to protect human participants from being enrolled in trials that are based on weak evidence”
From Investigating the replicability of preclinical cancer biology (2021) · cites this paper as Landis et al., 2012
By providing a comprehensive set of recommendations specifically tailored to the description of in vivo research, ARRIVE 2.0 helps authors reporting animal experiments adhere to NIH standards and the MDAR minimum standards framework and checklist.
“By providing a comprehensive set of recommendations that are specifically tailored to the description of in vivo research, they help authors reporting animal experiments adhere to the National Institutes of Health (NIH) standards [43] and the minimum standards framework and checklist (Materials, Design, Analysis and Reporting [MDAR] [47]).”
From The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research (2020) · cites this paper as [43]
The standards for statistical testing and raw data access have been adopted by the National Institutes of Health.
“The standards have been adopted by the National Institutes of Health (8,70).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [70]
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“portending a waning confidence in animal model data to identify promising therapeutic targets.(5,7,11,12,22,23) Cardiovascular research and development, in particular, has fared poorly in recent years relative to other disease categories, exhibiting the greatest decline as a percentage of total projects(4) and among t…”
InfluentialAn Analysis of Mesenchymal Stem Cell-Derived Extracellular Vesicles for Preclinical Use
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- Authors
- Landis, S. C., Amara, S. G., Asadullah, K., Austin, C. P., Blumenstein, R., Bradley, E. W., Crystal, R. G., Darnell, R. B., Ferrante, R. J., Fillit, H., Finkelstein, R., Fisher, M., Gendelman, H. E., Golub, R. M., Goudreau, J. L., Gross, R. A., Gubitz, A. K., Hesterlee, S. E., Howells, D. W., Huguenard, J., Kelner, K., Koroshetz, W., Krainc, D., Lazic, S. E., Levine, M. S., Macleod, M. R., McCall, J. M., Moxley III, R. T., Narasimhan, K., Noble, L. J., Perrin, S., Porter, J. D., Steward, O., Unger, E., Utz, U., & Silberberg, S. D.
- Affiliations
- National Institutes of Health (US)
- University of Pittsburgh (US)
- Bayer (Germany) (DE)
- CHDI Foundation (US)
- Cornell University (US)
- Howard Hughes Medical Institute (US)
- Alzheimer's Drug Discovery Foundation (US)
- University of Massachusetts Chan Medical School (US)
- University of Nebraska Medical Center (US)
- American Medical Association (US)
- Michigan State University (US)
- University of Rochester Medicine (US)
- Parent Project Muscular Dystrophy (US)
- The University of Melbourne (AU)
- Stanford University (US)
- American Association For The Advancement of Science (US)
- Harvard University (US)
- F. Hoffmann-La Roche AG (CH)
- University of California, Los Angeles (US)
- Western General Hospital (GB)
- Amyotrophic Lateral Sclerosis Therapy Development Institute (US)
- University of California, Irvine (US)
- United States Food and Drug Administration (US)
- Corresponding author
- Silberberg, Shai D.
- ORCID
- Amara, Susan G.
- Crystal, Ronald G.
- Darnell, Robert B.
- Fillit, Howard
- Fisher, Marc
- Gendelman, Howard E.
- Goudreau, John L.
- Gross, Robert A.
- Howells, David W.
- Huguenard, John
- Koroshetz, Walter
- Krainc, Dimitri
- Lazic, Stanley E.
- Macleod, Malcolm R.
- Porter, John D.
- Steward, Oswald
- Unger, Ellis
- Utz, Ursula
- Silberberg, Shai D.
- Venue
- Nature
- Published
- 2012-10
- Volume
- 490
- Issue
- 7419
- Pages
- 187-191
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3511845
- OpenAlex
- W2137516955
- Cited by
- 1,196
- References
- 71
- Citations by year
- 2026: 45 · 2025: 57 · 2024: 40 · 2023: 72 · 2022: 68 · 2021: 80 · 2020: 91 · 2019: 87 · 2018: 79 · 2017: 116 · 2016: 136 · 2015: 129 · 2014: 115 · 2013: 68 · 2012: 12
- FWCI
- 149.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion, cc-by-nc-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-sa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (Z99 NS999999, 5t32ns007280-35)
- National Institute of Mental Health (P30MH062261, T32MH020016)
- National Institute of Neurological Disorders and Stroke (T32NS-007280, P01NS012151, R01NS041574, R01NS034774, R01NS006477)
- National Center for Research Resources (UL1RR024160)
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- animal experimentation, preclinical research, reporting guidelines, reproducibility, sample size determination, randomization, blinding, data handling, translational research, grant applications, peer review
- MeSH terms
- Animals, Publishing, Random Allocation, Research Design, Statistics as Topic, Sample Size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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