The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Interpretation of tests of heterogeneity and bias in meta-analysis
Abstract
Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false-positive and false-negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.
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“These limitations are common in meta-analyses of cross-sectional studies (Ioannidis, 2008).”
From Decision making capacity for treatment in psychiatric inpatients: a systematic review and meta-analysis (2024) · cites this paper as Ioannidis, 2008
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Cites
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Why Most Published Research Findings Are False
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Self-citationThe combination of estimates from different experiments
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Why Most Published Research Findings Are False
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Self-citationAssessing heterogeneity in meta-analysis: Q statistic or I² index?
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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
Self-citationGloy, V. L., Briel, M., Bhatt, D. L., Kashyap, S. R., Schauer, P. R., Mingrone, G., Bucher, H. C., & Nordmann, A. J. (2013) · BMJ · doi:10.1136/bmj.f5934 · Cited by 1,351
Sonuga-Barke, E. J. S., Brandeis, D., Cortese, S., Daley, D., Ferrin, M., Holtmann, M., Stevenson, J., Danckaerts, M., van der Oord, S., Döpfner, M., et al. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12070991 · Cited by 1,167
“Given the relatively small number of methodologically sound studies, the field is not yet mature enough for the investigation of publication bias using funnel plots—the interpretation of which, moreover, is equivocal when based on a small number of studies (29).”
BackgroundA systematic review of personality trait change through intervention.
Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017) · Psychological Bulletin · doi:10.1037/bul0000088 · Cited by 1,047
“Most critically, Ioannidis (2008) argued that when effect sizes are even moderately heterogeneous, regression-based tests are “inappropriate, meaningless, or both” (p. 954).”
BackgroundZachariae, R., Lyby, M. S., Ritterband, L. M., & O'Toole, M. S. (2016) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2015.10.004 · Cited by 704
Metadata
- Authors
- Ioannidis, J. P. A.
- Affiliations
- University of Ioannina (GR)
- Corresponding author
- Ioannidis, John P. A.
- Venue
- Journal of Evaluation in Clinical Practice
- Publisher
- Wiley
- Published
- 2008-10
- Volume
- 14
- Issue
- 5
- Pages
- 951-957
- ISSN
- 1356-1294 (ISSN-L)
- 1365-2753
- OpenAlex
- W2144112749
- Cited by
- 696
- References
- 57
- Citations by year
- 2026: 52 · 2025: 69 · 2024: 98 · 2023: 72 · 2022: 50 · 2021: 64 · 2020: 49 · 2019: 34 · 2018: 30 · 2017: 23 · 2016: 23 · 2015: 26 · 2014: 27 · 2013: 22 · 2012: 15
- FWCI
- 4.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Evaluation in Clinical Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- heterogeneity testing, publication bias, meta-analysis, statistical heterogeneity, clinical heterogeneity, false positive signals, bias detection
- MeSH terms
- Data Interpretation, Statistical, Humans, Regression Analysis, Research Design, Risk, Sensitivity and Specificity, Meta-Analysis as Topic, Reproducibility of Results, Bias, Confidence Intervals, Publication Bias, Evidence-Based Medicine
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