The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
When should meta‐analysis avoid making hidden normality assumptions?
Open accessAbstract
Meta‐analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta‐analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta‐analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta‐analysis. We also hope that this article will stimulate further discussion and methodological work.
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Cites
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Methods to estimate the between‐study variance and its uncertainty in meta‐analysis
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Self-citationA refined method for the meta-analysis of controlled clinical trials with binary outcome
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metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression
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Lin, L., & Chu, H. (2020) · Epidemiology · doi:10.1097/ede.0000000000001232 · Cited by 345
Evaluation of various estimators for standardized mean difference in meta‐analysis
Lin, L., & Aloe, A. M. (2021) · Statistics in Medicine · doi:10.1002/sim.8781 · Cited by 297
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Held, L., Hofmann, F., & Pawel, S. (2025) · Research Synthesis Methods · doi:10.1017/rsm.2025.26 · Cited by 2
Metadata
- Authors
- Jackson, D., & White, I. R.
- Venue
- Biometrical Journal
- Publisher
- Wiley
- Published
- 2018-07-30
- Volume
- 60
- Issue
- 6
- Pages
- 1040-1058
- ISSN
- 0323-3847 (ISSN-L)
- 1521-4036
- OpenAlex
- W2886282933
- Cited by
- 178
- References
- 12
- FWCI
- 15.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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