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Using network meta‐analysis to evaluate the existence of small‐study effects in a network of interventions
Abstract
Suggested methods for exploring the presence of small‐study effects in a meta‐analysis and the possibility of publication bias are associated with important limitations. When a meta‐analysis comprises only a few studies, funnel plots are difficult to interpret, and regression‐based approaches to test and account for small‐study effects have low power. Assuming that the cause of funnel plot asymmetry is likely to affect an entire research field rather than only a particular comparison of interventions, we suggest that network meta‐regression is employed to account for small‐study effects in a set of related meta‐analyses. We present several possible models for the direction and distribution of small‐study effects and we describe the methods by re‐analysing two published networks. Copyright © 2012 John Wiley & Sons, Ltd.
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“Potential small-study effects, including possible publication bias, were explored using ‘comparison-adjusted’ funnel plots.”
From The Effect of Different Psychological Interventions on Athletes' Competitive Anxiety: A Systematic Review and Bayesian Network Meta-Analysis. (2026) · cites this paper as [43]
In the citing paper
“We evaluated the presence of small-study effect (publication bias) by means of comparison-adjusted funnel plots [13].”
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Cited by
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Hutton, B., Salanti, G., Caldwell, D. M., Chaimani, A., Schmid, C. H., Cameron, C., Ioannidis, J. P. A., Straus, S., Thorlund, K., Jansen, J. P., et al. (2015) · Annals of Internal Medicine · doi:10.7326/m14-2385 · Cited by 8,269
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Self-citationCINeMA: an approach for assessing confidence in the results of a network meta-analysis
Nikolakopoulou, A., Higgins, J. P. T., Papakonstantinou, T., Chaimani, A., Del Giovane, C., Egger, M., & Salanti, G. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003082 · Cited by 1,935
“• Empirical examination of patterns of results between small and large studies, using comparison-adjusted [35,36], regression [37], or selection [38] models, do not indicate that results from small studies differ from those in published studies.”
ResultSelf-citationEvaluating the quality of evidence from a network meta-analysis
Salanti, G., Del Giovane, C., Chaimani, A., Caldwell, D. M., & Higgins, J. P. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099682 · Cited by 1,402
“The comparison-adjusted funnel plot in Figure 7 presents the centered ln (OR) for pairwise comparisons of a ‘reference’ treatment versus an active or newer or ‘experimental’ treatment [30].”
BackgroundMethodSelf-citation
Metadata
- Authors
- Chaimani, A., & Salanti, G.
- Affiliations
- University of Ioannina (GR)
- Corresponding author
- Salanti, Georgia
- Venue
- Research Synthesis Methods
- Publisher
- Wiley
- Published
- 2012-06
- Volume
- 3
- Issue
- 2
- Pages
- 161-176
- ISSN
- 1759-2879 (ISSN-L)
- 1759-2887
- OpenAlex
- W2121796373
- Cited by
- 521
- References
- 35
- Citations by year
- 2026: 59 · 2025: 48 · 2024: 55 · 2023: 59 · 2022: 50 · 2021: 60 · 2020: 58 · 2019: 37 · 2018: 28 · 2017: 29 · 2016: 17 · 2015: 4 · 2014: 8 · 2013: 7 · 2012: 2
- FWCI
- 8.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Research Synthesis Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- uropean Research Council (IMMA 260559)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- small-study effects, publication bias, network meta-regression, network meta-analysis, funnel plot asymmetry
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