Evaluation of excess significance bias in animal studies of neurological diseases
Open accessAbstract
Animal studies generate valuable hypotheses that lead to the conduct of preventive or therapeutic clinical trials. We assessed whether there is evidence for excess statistical significance in results of animal studies on neurological disorders, suggesting biases. We used data from meta-analyses of interventions deposited in Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Studies (CAMARADES). The number of observed studies with statistically significant results (O) was compared with the expected number (E), based on the statistical power of each study under different assumptions for the plausible effect size. We assessed 4,445 datasets synthesized in 160 meta-analyses on Alzheimer disease (n = 2), experimental autoimmune encephalomyelitis (n = 34), focal ischemia (n = 16), intracerebral hemorrhage (n = 61), Parkinson disease (n = 45), and spinal cord injury (n = 2). 112 meta-analyses (70%) found nominally (p≤0.05) statistically significant summary fixed effects. Assuming the effect size in the most precise study to be a plausible effect, 919 out of 4,445 nominally significant results were expected versus 1,719 observed (p<10⁻⁹). Excess significance was present across all neurological disorders, in all subgroups defined by methodological characteristics, and also according to alternative plausible effects. Asymmetry tests also showed evidence of small-study effects in 74 (46%) meta-analyses. Significantly effective interventions with more than 500 animals, and no hints of bias were seen in eight (5%) meta-analyses. Overall, there are too many animal studies with statistically significant results in the literature of neurological disorders. This observation suggests strong biases, with selective analysis and outcome reporting biases being plausible explanations, and provides novel evidence on how these biases might influence the whole research domain of neurological animal literature.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Analysing the data using different methods and selectively reporting those with statistically significant results constitutes p-hacking and introduces bias in the literature.
“Analysing the data using different methods and selectively reporting those with statistically significant results constitutes p-hacking and introduces bias in the literature [90,94].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [90]
Failure to disclose all the outcomes that were measured introduces bias in the literature, as positive outcomes are reported more often.
“Failure to disclose all the outcomes that were measured introduces bias in the literature, as positive outcomes (e.g., those statistically significant) are reported more often [88–91].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [90]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139 · see citing works
Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722 · see citing works
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552 · see citing works
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763 · see citing works
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048 · see citing works
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rucker, G., Harbord, R. M., Schmid, C. H., et al. (2011) · BMJ · doi:10.1136/bmj.d4002 · Cited by 7,709 · see citing works
Self-citationImproving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M., & Altman, D. G. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000412 · Cited by 7,300 · see citing works
Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M., & Altman, D. G. (2010) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.4103/0976-500x.72351 · Cited by 6,277 · see citing works
The combination of estimates from different experiments
Cochran, W. G. (1954) · Biometrics · doi:10.2307/3001666 · Cited by 5,380 · see citing works
GPOWER: A general power analysis program
Erdfelder, E., Faul, F., & Buchner, A. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03203630 · Cited by 4,864 · see citing works
The case of the misleading funnel plot
Lau, J., Ioannidis, J. P. A., Terrin, N., Schmid, C. H., & Olkin, I. (2006) · BMJ · doi:10.1136/bmj.333.7568.597 · Cited by 1,766 · see citing works
Self-citationWhy Most Discovered True Associations Are Inflated
Ioannidis, J. P. A. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818131e7 · Cited by 1,495 · see citing works
Self-citation1,026 Experimental treatments in acute stroke
O’Collins, V. E., Macleod, M. R., Donnan, G. A., Horky, L. L., van der Worp, H. B., & Howells, D. W. (2006) · Annals of Neurology · doi:10.1002/ana.20741 · Cited by 1,453 · see citing works
Self-citationCan Animal Models of Disease Reliably Inform Human Studies?
van der Worp, H. B., Howells, D. W., Sena, E. S., Porritt, M. J., Rewell, S., O'Collins, V., & Macleod, M. R. (2010) · PLoS Medicine · doi:10.1371/journal.pmed.1000245 · Cited by 1,354 · see citing works
Self-citationRoundtable, S. T. A. I. (1999) · Stroke · doi:10.1161/01.str.30.12.2752 · Cited by 1,258 · see citing works
Uncertainty in heterogeneity estimates in meta-analyses
Ioannidis, J. P. A., Patsopoulos, N. A., & Evangelou, E. (2007) · BMJ · doi:10.1136/bmj.39343.408449.80 · Cited by 1,254 · see citing works
Self-citationA call for transparent reporting to optimize the predictive value of preclinical research
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., et al. (2012) · Nature · doi:10.1038/nature11556 · Cited by 1,196 · see citing works
Self-citationThe appropriateness of asymmetry tests for publication bias in meta-analyses: A large survey
Ioannidis, J. P. A., & Trikalinos, T. A. (2007) · Canadian Medical Association Journal · doi:10.1503/cmaj.060410 · Cited by 1,052 · cited 1× · see citing works
“However, these methods may not be very sensitive or specific in the detection of such biases, especially when few studies are included in a meta-analysis [11–13].”
MethodSelf-citationComparison of Treatment Effects between Animal Experiments and Clinical Trials: Systematic Review
Perel, P., Roberts, I., Sena, E., Wheble, P., Briscoe, C., Sandercock, P., Macleod, M., Mignini, L. E., Jayaram, P., & Khan, K. S. (2007) · BMJ · doi:10.1136/bmj.39048.407928.be · Cited by 827 · see citing works
Self-citationAn exploratory test for an excess of significant findings
Ioannidis, J. P., & Trikalinos, T. A. (2007) · Clinical Trials · doi:10.1177/1740774507079441 · Cited by 751 · see citing works
Self-citationTranslation of Research Evidence From Animals to Humans
Hackam, D. G., & Redelmeier, D. A. (2006) · JAMA · doi:10.1001/jama.296.14.1731 · Cited by 733 · see citing works
Pooling of Animal Experimental Data Reveals Influence of Study Design and Publication Bias
Macleod, M., O'Collins, T., Howells, D., & Donnan, G. (2004) · Stroke · doi:10.1161/01.str.0000125719.25853.20 · Cited by 692 · see citing works
Self-citationPublication bias in reports of animal stroke studies leads to major overstatement of efficacy
Sena, E. S., van der Worp, H. B., Bath, P. M. W., Howells, D. W., & Macleod, M. R. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000344 · Cited by 600 · cited 1× · see citing works
“The estimate from this most precise study, other things being equal, should be closer to the true estimate than the results of less precise studies, especially if biases affect predominantly the literature of smaller studies (small-study effects) [44–46].”
ResultSelf-citationWhere is the evidence that animal research benefits humans?
Pound, P., Ebrahim, S. B. J., Sandercock, P. A., Bracken, M. B., & Roberts, I. (2004) · BMJ · doi:10.1136/bmj.328.7438.514 · Cited by 507 · see citing works
Minocycline treatment in acute stroke
Lampl, Y., Boaz, M., Gilad, R., Lorberboym, M., Dabby, R., Rapoport, A. M., Anca-Hershkowitz, M., & Sadeh, M. (2007) · Neurology · doi:10.1212/01.wnl.0000277487.04281.db · Cited by 443 · see citing works
How can we improve the pre-clinical development of drugs for stroke?
Sena, E. S., van der Worp, H. B., Howells, D. W., & Macleod, M. R. (2007) · Trends in Neurosciences · doi:10.1016/j.tins.2007.06.009 · Cited by 365 · see citing works
Self-citationGood Laboratory Practice: Preventing Introduction of Bias at the Bench
Macleod, M. R., Fisher, M., O'Collins, V., Sena, E. S., Dirnagl, U., Bath, P. M. W., Buchan, A., van der Worp, H. B., Traystman, R., Minematsu, K., et al. (2009) · Stroke · doi:10.1161/strokeaha.108.525386 · Cited by 331 · see citing works
Self-citationMacleod, M. R., van der Worp, H. B., Sena, E. S., Howells, D. W., Dirnagl, U., & Donnan, G. A. (2008) · Stroke · doi:10.1161/strokeaha.108.515957 · Cited by 319 · see citing works
Self-citationExcess Significance Bias in the Literature on Brain Volume Abnormalities
Ioannidis, J. P. A. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.28 · Cited by 299 · see citing works
Self-citationEmergency medicine animal research: does use of randomization and blinding affect the results?
Bebarta, V., Luyten, D., & Heard, K. (2003) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2003.tb00056.x · Cited by 244 · see citing works
Whitney, E. J., Krasuski, R. A., Personius, B. E., Michalek, J. E., Maranian, A. M., Kolasa, M. W., Monick, E. A., Brown, B. G., & Gotto, A. M. (2005) · Annals of Internal Medicine · doi:10.7326/0003-4819-142-2-200501180-00008 · Cited by 231 · see citing works
Empirical Evidence of Bias in the Design of Experimental Stroke Studies
Crossley, N. A., Sena, E. S., Göhler, J., Horn, J., van der Worp, H. B., Bath, P. M., Macleod, M. R., & Dirnagl, U. (2008) · Stroke · doi:10.1161/strokeaha.107.498725 · Cited by 231 · see citing works
Self-citationGranulocyte colony-stimulating factor for acute ischemic stroke: a randomized controlled trial
Shyu, W. (2006) · Canadian Medical Association Journal · doi:10.1503/cmaj.051322 · Cited by 211 · see citing works
Systematic Review and Metaanalysis of the Efficacy of FK506 in Experimental Stroke
Macleod, M., O'Collins, T., Horky, L. L., Howells, D., & Donnan, G. (2005) · Journal of Cerebral Blood Flow and Metabolism · doi:10.1038/sj.jcbfm.9600064 · Cited by 196 · see citing works
Self-citationImproving the translational hit of experimental treatments in multiple sclerosis
Vesterinen, H. M., Sena, E. S., ffrench-Constant, C., Williams, A., Chandran, S., & Macleod, M. R. (2010) · Multiple Sclerosis Journal · doi:10.1177/1352458510379612 · Cited by 182 · see citing works
Self-citationA phase III study evaluating the efficacy and safety of MBP8298 in secondary progressive MS
Freedman, M. S., Bar‐Or, A., Oger, J. J.-F., Traboulsee, A. L., Patry, D., Young, C. A., Olsson, T. P., Li, D., Hartung, H. ‐., Krantz, M. J., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318233b240 · Cited by 137 · see citing works
Systematic review and meta‐analysis of the efficacy of melatonin in experimental stroke
Macleod, M. R., O'Collins, T., Horky, L. L., Howells, D., & Donnan, G. A. (2005) · Journal of Pineal Research · doi:10.1111/j.1600-079x.2004.00172.x · Cited by 137 · see citing works
Self-citationOno, H., Osanai, T., Ishizaka, H., Hanada, H., Kamada, T., Onodera, H., Fujita, N., Sasaki, S., Matsunaga, T., & Okumura, K. (2004) · American Heart Journal · doi:10.1016/j.ahj.2004.05.014 · Cited by 126 · see citing works
Extrapolating from animals to humans
Ioannidis, J. P. A. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3004631 · Cited by 124 · see citing works
Self-citationSena, E., Briscoe, C., Howells, D., Donnan, G., Sandercock, P., & Macleod, M. (2010) · Journal of Cerebral Blood Flow and Metabolism · doi:10.1038/jcbfm.2010.116 · Cited by 118 · see citing works
Self-citationEfficacy of minocycline in acute ischemic stroke: A single-blinded, placebo-controlled trial
Srivastava, M. V. P., Bhasin, A., Bhatia, R., Garg, A. K., Gaikwad, S. B., Prasad, K., Singh, M. B., & Tripathi, M. (2012) · Neurology India · doi:10.4103/0028-3886.93584 · Cited by 112 · see citing works
Granulocyte-Colony Stimulating Factor for Mobilizing Bone Marrow Stem Cells in Subacute Stroke
England, T. J., Abaei, M., Auer, D. P., Lowe, J. S., Jones, D., Sare, G. M., Walker, M. F., & Bath, P. M. (2011) · Stroke · doi:10.1161/strokeaha.111.636449 · Cited by 98 · see citing works
O'Collins, V. E., Macleod, M., Cox, S. F., van Raay, L., Aleksoska, E., Donnan, G., & Howells, D. (2011) · Journal of Cerebral Blood Flow and Metabolism · doi:10.1038/jcbfm.2010.184 · Cited by 95 · see citing works
Self-citationTsilidis, K. K., Papatheodorou, S., Εvangelou, Ε., & Ioannidis, J. P. A. (2012) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djs437 · Cited by 93 · see citing works
Self-citationDopamine agonists in animal models of Parkinson’s disease: A systematic review and meta-analysis
Rooke, E. D., Vesterinen, H. M., Sena, E. S., Egan, K. J., & Macleod, M. R. (2011) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2011.02.010 · Cited by 78 · see citing works
Self-citationSugimoto, K., Ito, H., Iwakura, K., Ikushima, M., Kato, A., Kimura, R., Tanaka, K., Masuyama, T., Ogihara, T., Kawano, S., et al. (2003) · Circulation Journal · doi:10.1253/circj.67.295 · Cited by 69 · see citing works
Kavvoura, F. K., McQueen, M. B., Khoury, M. J., Tanzi, R. E., Bertram, L., & Ioannidis, J. P. A. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn206 · Cited by 47 · see citing works
Self-citationKücükakin, B., Wilhelmsen, M., Lykkesfeldt, J., Reiter, R. J., Rosenberg, J., & Gögenür, I. (2010) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2010.06.014 · Cited by 33 · see citing works
Libman, I., Gawel, M. J., Riopelle, R. J., & Bouchard, S. (1987) · PubMed · Cited by 14 · see citing works
Heller, G. (2005) · Statistics in Medicine · doi:10.1002/sim.2268 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [45]Sena ES, van der Worp HB, Bath PM, Howells DW, Macleod MR (2010) Publication bias in reports of animal stroke studies leads to major overstatement of efficacy. PLoS Biol 8: e1000344 doi: 10.1371/journal.pbio.1000344 20361022 PMC2846857 · DOI · Paper page
- [47]Higgins J, Green S, editors (2008) Cochrane Handbook for Systematic Reviews of Interventions. Chichester, England: The Cochrane Collaboration and John Wiley & Sons Ltd.
Cited by
Sorted by citations
Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0
Percie du Sert, N., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000411 · Cited by 2,677 · see citing works
“Analysing the data using different methods and selectively reporting those with statistically significant results constitutes p-hacking and introduces bias in the literature [90,94].”
MethodSelf-citationThe Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta‐analyses
IOANNIDIS, J. P. A. (2016) · The Milbank Quarterly · doi:10.1111/1468-0009.12210 · Cited by 1,483 · see citing works
Self-citationAnalysis of drug combinations: current methodological landscape
Foucquier, J., & Guedj, M. (2015) · Pharmacology Research & Perspectives · doi:10.1002/prp2.149 · Cited by 1,115 · see citing works
Reproducibility in Science: Improving the Standard for Basic and Preclinical Research
Begley, C. G., & Ioannidis, J. P. A. (2015) · Circulation Research · doi:10.1161/circresaha.114.303819 · Cited by 1,035 · see citing works
Self-citationThe ARRIVE guidelines 2019: updated guidelines for reporting animal researchPreprint
du Sert, N. P., Hurst, V., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/703181 · Cited by 1,042 · see citing works
Self-citationExperimental design and analysis and their reporting: new guidance for publication in BJP
Curtis, M. J., Bond, R. A., Spina, D., Ahluwalia, A., Alexander, S. P. H., Giembycz, M. A., Gilchrist, A., Hoyer, D., Insel, P. A., Izzo, A. A., et al. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12856 · Cited by 1,034 · see citing works
Theodoratou, E., Tzoulaki, I., Zgaga, L., & Ioannidis, J. P. A. (2014) · BMJ · doi:10.1136/bmj.g2035 · Cited by 956 · see citing works
Self-citationType 2 diabetes and cancer: umbrella review of meta-analyses of observational studies
Tsilidis, K. K., Kasimis, J. C., López, D. S., Ntzani, E., & Ioannidis, J. P. A. (2015) · BMJ · doi:10.1136/bmj.g7607 · Cited by 861 · see citing works
Self-citationAdiposity and cancer at major anatomical sites: umbrella review of the literature
Kyrgiou, M., Kalliala, I., Markozannes, G., Gunter, M. J., Paraskevaidis, E., Gabra, H., Martin-Hirsch, P., & Tsilidis, K. K. (2017) · BMJ · doi:10.1136/bmj.j477 · Cited by 803 · see citing works
Self-citationBelbasis, L., Bellou, V., Εvangelou, Ε., Ioannidis, J. P. A., & Tzoulaki, I. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70267-4 · Cited by 669 · see citing works
Self-citationA solution to dependency: using multilevel analysis to accommodate nested data
Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V., & van der Sluis, S. (2014) · Nature Neuroscience · doi:10.1038/nn.3648 · Cited by 631 · see citing works
Inconsistencies and Controversies Surrounding the Amyloid Hypothesis of Alzheimer's Disease
Morris, G. P., Clark, I. A., & Vissel, B. (2014) · Acta Neuropathologica Communications · doi:10.1186/s40478-014-0135-5 · Cited by 522 · see citing works
Meta-analysis of data from animal studies: A practical guide
Vesterinen, H. M., Sena, E. S., Egan, K. J., Hirst, T. C., Churolov, L., Currie, G. L., Antonic‐Baker, A., Howells, D. W., & Macleod, M. R. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.09.010 · Cited by 477 · see citing works
Self-citationEvolution of Reporting <i>P</i> Values in the Biomedical Literature, 1990-2015
Chavalarias, D., Wallach, J. D., Li, A. H. T., & Ioannidis, J. P. A. (2016) · JAMA · doi:10.1001/jama.2016.1952 · Cited by 370 · see citing works
Self-citationRisk of bias in reports of in vivo research: a focus for improvement
Macleod, M. R., Lawson McLean, A., Kyriakopoulou, A., Serghiou, S., de Wilde, A., Sherratt, N., Hirst, T., Hemblade, R., Bahor, Z., Nunes-Fonseca, C., et al. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002273 · Cited by 358 · see citing works
Self-citationType 2 Diabetes and Cancer: An Umbrella Review of Observational and Mendelian Randomization Studies
Pearson‐Stuttard, J., Papadimitriou, N., Markozannes, G., Cividini, S., Κακούρου, Α., Gill, D., Rizos, E. C., Monori, G., Ward, H. A., Kyrgiou, M., et al. (2021) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-1245 · Cited by 277 · see citing works
Self-citationNo raw data, no science: another possible source of the reproducibility crisis
Miyakawa, T. (2020) · Molecular Brain · doi:10.1186/s13041-020-0552-2 · Cited by 268 · see citing works
Sena, E. S., Currie, G. L., McCann, S. K., Macleod, M. R., & Howells, D. W. (2014) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2014.28 · Cited by 264 · see citing works
Self-citationLlovera, G., Hofmann, K., Roth, S., Salas-Pérdomo, A., Ferrer-Ferrer, M., Perego, C., Zanier, E., Mamrak, U., Rex, A., Party, H., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9853 · Cited by 252 · see citing works
Hall, W., Carter, A., & Forlini, C. (2015) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)00126-6 · Cited by 233 · see citing works
p-Curve and p-Hacking in Observational Research
Bruns, S. B., & Ioannidis, J. P. A. (2016) · PLOS ONE · doi:10.1371/journal.pone.0149144 · Cited by 210 · see citing works
Self-citationIn Vivo Imaging of Human Neuroinflammation
Albrecht, D., Granziera, C., Hooker, J. M., & Loggia, M. L. (2016) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.6b00056 · Cited by 207 · see citing works
Is animal research sufficiently evidence based to be a cornerstone of biomedical research?
Pound, P., & Bracken, M. B. (2014) · BMJ · doi:10.1136/bmj.g3387 · Cited by 207 · see citing works
Dirnagl, U., & Endres, M. (2014) · Stroke · doi:10.1161/strokeaha.113.004075 · Cited by 194 · see citing works
Carbone, L., & Austin, J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0155001 · Cited by 191 · see citing works
Improving the translation of analgesic drugs to the clinic: animal models of neuropathic pain
du Sert, N. P., & Rice, A. S. C. (2014) · British Journal of Pharmacology · doi:10.1111/bph.12645 · Cited by 188 · see citing works
Ioannidis, J. P. A., Kim, B. Y. S., & Trounson, A. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0314-y · Cited by 175 · see citing works
Self-citationNecka, E. A., Cacioppo, S., Norman, G. J., & Cacioppo, J. T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157732 · Cited by 161 · see citing works
Opportunities and challenges in modeling human brain disorders in transgenic primates
Jennings, C. G. B., Landman, R., Zhou, Y., Sharma, J., Hyman, J. B., Movshon, J. A., Qiu, Z., Roberts, A., Roe, A. W., Wang, X., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4362 · Cited by 140 · see citing works
Editorial: preclinical data reproducibility for R&D - the challenge for neuroscience
Steckler, T. (2015) · SpringerPlus · doi:10.1186/2193-1801-4-1 · Cited by 141 · see citing works
Meta-analyses of whale-watching impact studies: comparisons of cetacean responses to disturbance
Senigaglia, V., Christiansen, F., Bejder, L., Gendron, D., Lundquist, D., Noren, D. P., Schaffar, A., Smith, J., Williams, R., Martinez, E., et al. (2015) · Marine Ecology Progress Series · doi:10.3354/meps11497 · Cited by 131 · see citing works
Bringing rigour to translational medicine
Howells, D. W., Sena, E. S., & Macleod, M. R. (2014) · Nature Reviews Neurology · doi:10.1038/nrneurol.2013.232 · Cited by 130 · see citing works
Self-citationLai, M.-C., Chandrasekera, P. C., & Barnard, N. D. (2014) · Nutrition and Diabetes · doi:10.1038/nutd.2014.30 · Cited by 129 · see citing works
Preclinical Models of Alzheimer's Disease: Relevance and Translational Validity
Mullane, K., & Williams, M. (2019) · Current Protocols in Pharmacology · doi:10.1002/cpph.57 · Cited by 122 · see citing works
More than 3Rs: The importance of scientific validity for harm-benefit analysis of animal research
Würbel, H. (2017) · Lab Animal · doi:10.1038/laban.1220 · Cited by 152 · see citing works
Holman, C., Piper, S. K., Grittner, U., Diamantaras, A. A., Kimmelman, J., Siegerink, B., & Dirnagl, U. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002331 · Cited by 114 · see citing works
Sedentary behavior and cancer–an umbrella review and meta-analysis
Hermelink, R., Leitzmann, M. F., Markozannes, G., Tsilidis, K. K., Pukrop, T., Berger, F. F., Baurecht, H., & Jochem, C. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-022-00873-6 · Cited by 112 · see citing works
Self-citationDoes psychotherapy work? An umbrella review of meta‐analyses of randomized controlled trials
Dragioti, E., Karathanos, V. T., Gerdle, B. U. C., & Εvangelou, Ε. (2017) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12713 · Cited by 112 · see citing works
Sonzogni, M., Wallaard, I., Santos, S. S., Kingma, J., du Mee, D. J. M., van Woerden, G. M., & Elgersma, Y. (2018) · Molecular Autism · doi:10.1186/s13229-018-0231-7 · Cited by 107 · see citing works
Refinement of experimental design and conduct in laboratory animal research.
Bailoo, J. D., Reichlin, T. S., & Würbel, H. (2014) · ILAR journal · doi:10.1093/ilar/ilu037 · Cited by 101 · see citing works
Reproducibility and Rigor in Animal-Based Research
Macleod, M., & Mohan, S. (2019) · ILAR Journal · doi:10.1093/ilar/ilz015 · Cited by 104 · see citing works
Self-citationMcCafferty, K., Forbes, S. H., Thiemermann, C., & Yaqoob, M. M. (2014) · Disease Models & Mechanisms · doi:10.1242/dmm.016741 · Cited by 103 · see citing works
Understanding the roles of mutations in the amyloid precursor protein in Alzheimer disease
Hunter, S., & Brayne, C. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.218 · Cited by 94 · see citing works
Gao, C., Guo, J., Gong, T.-T., Lv, J.-L., Li, X.-Y., Liu, F.-H., Zhang, M., Shan, Y.-T., Zhao, Y.-H., & Wu, Q.-J. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.813943 · Cited by 88 · see citing works
Progesterone neuroprotection: The background of clinical trial failure
Schumacher, M., Denier, C., Oudinet, J., Adams, D., & Guennoun, R. (2015) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2015.11.010 · Cited by 86 · see citing works
Empirical evaluation of which trial characteristics are associated with treatment effect estimates
Dechartres, A., Trinquart, L., Faber, T., & Ravaud, P. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.04.005 · Cited by 85 · see citing works
Pre-Clinical Testing of Therapies for Traumatic Brain Injury
DeWitt, D. S., Hawkins, B. E., Dixon, C. E., Kochanek, P. M., Armstead, W. M., Bass, C. R., Bramlett, H. M., Büki, A., Dietrich, W. D., Ferguson, A. R., et al. (2018) · Journal of Neurotrauma · doi:10.1089/neu.2018.5778 · Cited by 85 · see citing works
Drug delivery of the future: Chasing the invisible gorilla
Park, K. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.10.048 · Cited by 84 · see citing works
All (animal) models (of neurodegeneration) are wrong. Are they also useful?
Ransohoff, R. M. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20182042 · Cited by 80 · see citing works
Experimental autoimmune encephalomyelitis is a good model of multiple sclerosis if used wisely.
Baker, D., & Amor, S. (2014) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2014.05.002 · Cited by 76 · see citing works
Retrospective harm benefit analysis of pre-clinical animal research for six treatment interventions
Pound, P., & Nicol, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193758 · Cited by 50 · see citing works
Mentis, A. A., Dardiotis, E., Efthymiou, V., & Chrousos, G. P. (2021) · BMC Medicine · doi:10.1186/s12916-020-01873-7 · Cited by 65 · see citing works
Epstein–Barr virus and multiple sclerosis. From evidence to therapeutic strategies
Menéndez, S. F., Fernández-Morán, M., Fernández‐Vega, I., Pérez-Álvarez, Á. I., & Villafani, J. (2016) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2016.01.013 · Cited by 61 · see citing works
Controlling Brain Cells With Light: Ethical Considerations for Optogenetic Clinical Trials
Gilbert, F., Harris, A. R., & Kapsa, R. M. I. (2014) · AJOB Neuroscience · doi:10.1080/21507740.2014.911213 · Cited by 58 · see citing works
Be positive about negatives–recommendations for the publication of negative (or null) results
Bespalov, A., Steckler, T., & Skolnick, P. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2019.10.007 · Cited by 59 · see citing works
Markozannes, G., Aretouli, E., Rintou, E., Dragioti, E., Damigos, D., Ntzani, E., Εvangelou, Ε., & Tsilidis, K. K. (2017) · BMC Psychology · doi:10.1186/s40359-017-0200-5 · Cited by 58 · see citing works
Self-citationLlovera, G., & Liesz, A. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13516 · Cited by 53 · see citing works
Can prospective systematic reviews of animal studies improve clinical translation?
Pound, P., & Ritskes-Hoitinga, M. J. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02205-x · Cited by 55 · see citing works
Marano, L., Fusario, D., Savelli, V., Marrelli, D., & Roviello, F. (2021) · Updates in Surgery · doi:10.1007/s13304-021-01059-7 · Cited by 55 · see citing works
The Future of Preclinical Animal Models in Pharmaceutical Discovery and Development
Everitt, J. I. (2014) · Toxicologic Pathology · doi:10.1177/0192623314555162 · Cited by 56 · see citing works
Toxicological and pharmacologic effects of farnesol (C15H26O): A descriptive systematic review
Delmondes, G. D. A., Bezerra, D. S., Dias, D. D. Q., Borges, A. D. S., Araújo, I. M., da Cunha, G. L., Bandeira, P. F. R., Barbosa, R., Coutinho, H. D. M., Felipe, C. F. B., et al. (2019) · Food and Chemical Toxicology · doi:10.1016/j.fct.2019.04.037 · Cited by 55 · see citing works
Gao, X., Xu, J., Yao, T., Liu, X., Zhang, H., & Zhan, C. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114362 · Cited by 55 · see citing works
Singh, V. P., Pratap, K., Sinha, J., Desiraju, B. K., Bahal, D., & Kukreti, R. (2016) · International Journal of Immunopathology and Pharmacology · doi:10.1177/0394632016671728 · Cited by 54 · see citing works
Effectiveness of multidisciplinary programmes for clinical pain conditions: an umbrella review.
Dragioti, E., Evangelou, E., Larsson, B., & Gerdle, B. (2018) · Journal of Rehabilitation Medicine · doi:10.2340/16501977-2377 · Cited by 53 · see citing works
Systems Approach to Neurodegenerative Disease Biomarker Discovery
Lausted, C. G., Lee, I., Zhou, Y. F., Qin, S., Sung, J., Price, N. D., Hood, L., & Wang, K. (2013) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-011613-135928 · Cited by 52 · see citing works
Smith, M. M., Clarke, E., & Little, C. B. (2016) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2016.10.016 · Cited by 52 · see citing works
Bacterial meningitis: an update of new treatment options
Nau, R., Djukic, M., Spreer, A., Ribes, S., & Eiffert, H. (2015) · Expert Review of Anti-infective Therapy · doi:10.1586/14787210.2015.1077700 · Cited by 51 · see citing works
Efficacy of Alteplase in a Mouse Model of Acute Ischemic Stroke
Orset, C., Haelewyn, B., Allan, S. M., Ansar, S., Campos, F. A. D. M., Cho, T., Durand, A., Amki, M. E., Fatar, M., García‐Yébenes, I., et al. (2016) · Stroke · doi:10.1161/strokeaha.116.012238 · Cited by 49 · see citing works
Green, S. B. (2015) · BMC Medical Ethics · doi:10.1186/s12910-015-0043-7 · Cited by 48 · see citing works
Bahadoran, Z., Mirmiran, P., Kashfi, K., & Ghasemi, A. (2020) · Journal of the American Association for Laboratory Animal Science · doi:10.30802/aalas-jaalas-19-000139 · Cited by 49 · see citing works
Krafczyk, M. S., Shi, A., Bhaskar, A., Marinov, D., & Stodden, V. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0069 · Cited by 46 · see citing works
Experimental and Computational Methods for the Study of Cerebral Organoids: A Review
Poli, D., Magliaro, C., & Ahluwalia, A. D. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00162 · Cited by 46 · see citing works
Zhang, Y., & Ma, N. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.680833 · Cited by 45 · see citing works
Yao, M., Yang, L., Wang, J., Sun, Y., Dun, R.-L., Wang, Y., & Cui, X. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2014.3520 · Cited by 45 · see citing works
Freyne, B., Marchant, A., & Curtis, N. C. (2015) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1093/trstmh/tru197 · Cited by 44 · see citing works
Maxwell, E. K., Schnitzler, C. E., Havlak, P., Putnam, N. H., Nguyen, A., Moreland, R. T., & Baxevanis, A. D. (2014) · BMC Evolutionary Biology · doi:10.1186/s12862-014-0212-1 · Cited by 44 · see citing works
Unbridle biomedical research from the laboratory cage
Lahvis, G. P. (2017) · eLife · doi:10.7554/elife.27438 · Cited by 43 · see citing works
Farrell, K. E., Keely, S., Graham, B. A., Callister, R., & Callister, R. J. (2013) · Inflammatory Bowel Diseases · doi:10.1097/01.mib.0000437499.52922.b1 · Cited by 43 · see citing works
Zeiss, C. J., Allore, H. G., & Beck, A. P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171790 · Cited by 45 · see citing works
Fornaro, M., Dragioti, E., Prisco, M. D., Billeci, M., Mondin, A. M., Calati, R., Smith, L., Hatcher, S., Kaluzienski, M., Fiedorowicz, J. G., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02423-z · Cited by 43 · see citing works
Aligning physiology with psychology: Translational neuroscience in neuropsychiatric drug discovery
McArthur, R. A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.004 · Cited by 42 · see citing works
A meta-analysis of threats to valid clinical inference in preclinical research of sunitinib
Henderson, V. C., Demko, N., Hakala, A., MacKinnon, N., Federico, C. A., Fergusson, D. A., & Kimmelman, J. (2015) · eLife · doi:10.7554/elife.08351 · Cited by 42 · see citing works
Ting, K., Hill, C. L., & Whittle, S. (2015) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.12699 · Cited by 42 · see citing works
de Carvalho, F. O., Felipe, F. A., Costa, A. C. S. D. M., Teixeira, L. G. B., Silva, É. R., Nunes, P. S., Shanmugam, S., de Lucca, W., Quintans, J. D. S. S., & Araújo, A. A. D. S. (2016) · Lung · doi:10.1007/s00408-016-9879-y · Cited by 42 · see citing works
(PPR), E. P. O. P. P. P. A. T. R., Ockleford, C., Adriaanse, P., Berny, P. J., Brock, T. C. M., Duquesne, S., Grilli, S., Bennekou, S. H., Klein, M., Kühl, T., et al. (2017) · EFSA Journal · doi:10.2903/j.efsa.2017.5007 · Cited by 41 · see citing works
van der Naald, M., Wenker, S., Doevendans, P. A., Wever, K. E., & Chamuleau, S. A. J. (2020) · BMJ Open Science · doi:10.1136/bmjos-2019-100051 · Cited by 40 · see citing works
Animal models of cardiac arrest: A systematic review of bias and reporting
Fabian-Jessing, B. K., Vallentin, M. F., Secher, N., Hansen, F. B., Dezfulian, C., Granfeldt, A., & Andersen, L. W. (2018) · Resuscitation · doi:10.1016/j.resuscitation.2018.01.047 · Cited by 39 · see citing works
Neuroprotective Effect of Natural Products on Peripheral Nerve Degeneration: A Systematic Review
Araújo-Filho, H. G., Quintans‐Júnior, L. J., Barreto, A. S., Almeida, J. R. G. D. S., Barreto, R. D. S. S., & Quintans, J. D. S. S. (2015) · Neurochemical Research · doi:10.1007/s11064-015-1771-2 · Cited by 39 · see citing works
Zang, S., Wu, Q., Li, X., Gao, C., Liu, Y., Jiang, Y., Zhang, J., Sun, H. J., Chang, Q., & Zhao, Y. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152381 · Cited by 37 · see citing works
Amad, A., Jardri, R., Rousseau, C., Larochelle, Y., Ioannidis, J. P. A., & Naudet, F. (2019) · Psychotherapy and Psychosomatics · doi:10.1159/000502805 · Cited by 36 · see citing works
Self-citationFabbri, R., Cacopardo, L., Ahluwalia, A. D., & Magliaro, C. (2023) · Cells · doi:10.3390/cells12081181 · Cited by 37 · see citing works
Translational paradigms in pharmacology and drug discovery
Mullane, K., Winquist, R. J., & Williams, M. J. (2013) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.10.019 · Cited by 34 · see citing works
Wieschowski, S., Silva, D. S., & Strech, D. (2016) · PLoS Biology · doi:10.1371/journal.pbio.2000391 · Cited by 33 · see citing works
The pain of pain: Challenges of animal behavior models
Barrett, J. E. (2015) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2014.11.046 · Cited by 33 · see citing works
Ganna, A., Lee, D., Ingelsson, E., & Yudi Pawitan, Y. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu033 · Cited by 32 · see citing works
First-in-human HIV-remission studies: reducing and justifying risk
Dresser, R. S. (2016) · Journal of Medical Ethics · doi:10.1136/medethics-2015-103115 · Cited by 32 · see citing works
Dissemination Bias in Systematic Reviews of Animal Research: A Systematic Review
Mueller, K. F., Briel, M., Strech, D., Meerpohl, J. J., Lang, B., Motschall, E., Gloy, V., Lamontagne, F., & Bassler, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0116016 · Cited by 31 · see citing works
Lv, S., He, E., Luo, J., Liu, Y., Liang, W. J., Xu, S., Zhang, K., Yang, Y. F., Wang, M., Song, Y., et al. (2023) · Advanced Science · doi:10.1002/advs.202301828 · Cited by 29 · see citing works
Molecular Mechanisms of Renal Ischemic Conditioning Strategies
Kierulf-Lassen, C., Nieuwenhuijs‐Moeke, G. J., Krogstrup, N. V., Oltean, M., Jespersen, B., & Dor, F. J. M. F. (2015) · European Surgical Research · doi:10.1159/000437352 · Cited by 30 · see citing works
Moving towards less biased research
Yarborough, M. (2021) · BMJ Open Science · doi:10.1136/bmjos-2020-100116 · Cited by 26 · see citing works
- Authors
- Tsilidis, K. K., Panagiotou, O. A., Sena, E. S., Aretouli, E., Evangelou, E., Howells, D. W., Salman, R. A.-S., Macleod, M. R., & Ioannidis, J. P. A.
- Affiliations
- University of Ioannina (GR)
- The University of Melbourne (AU)
- Universidad de Deusto (ES)
- University of Edinburgh (GB)
- Stanford Medicine (US)
- Corresponding author
- Ioannidis, John P. A.
- ORCID
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-07-16
- Volume
- 11
- Issue
- 7
- Pages
- e1001609
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC3712913
- OpenAlex
- W2136462620
- Cited by
- 295
- References
- 50
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 14 · 2023: 25 · 2022: 13 · 2021: 19 · 2020: 21 · 2019: 25 · 2018: 19 · 2017: 27 · 2016: 31 · 2015: 31 · 2014: 36 · 2013: 10
- FWCI
- 39.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/K000659/1, NC/L000970/1)
- Medical Research Council
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- animal studies, neurological disorders, statistical power, selective reporting, small-study effects, Alzheimer's disease, experimental autoimmune encephalomyelitis, focal cerebral ischemia, intracerebral hemorrhage, Parkinson's disease, spinal cord injury
- MeSH terms
- Animals, Disease Models, Animal, Nervous System Diseases, Research Design, Bias
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 ›
Are you an author, or do you see a problem with this page? Request a review
