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A simple method for converting an odds ratio to effect size for use in meta-analysis
Abstract
A systematic review may encompass both odds ratios and mean differences in continuous outcomes. A separate meta-analysis of each type of outcome results in loss of information and may be misleading. It is shown that a ln(odds ratio) can be converted to effect size by dividing by 1.81. The validity of effect size, the estimate of interest divided by the residual standard deviation, depends on comparable variation across studies. If researchers routinely report residual standard deviation, any subsequent review can combine both odds ratios and effect sizes in a single meta-analysis when this is justified.
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“An example of a statistical method to convert an odds ratio to a standardised mean difference is that proposed by Chinn (2000).21”
From Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline (2020) · cites this paper as [21]
In the citing paper
“It is also possible to combine relative risks with other metrics like standardised mean differences or correlation coefficients. This requires transformation from an odds ratio in a standardised mean difference (or vice versa) [86] or an odds ratio in a correlation coefficient [87,88].”
From COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology (2019) · cites this paper as [86]
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Metadata
- Authors
- Chinn, S.
- Venue
- Statistics in Medicine
- Published
- 2000
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- CoChat. (2026). Citation check: A simple method for converting an odds ratio to effect size for use in meta-analysis. https://app.cochat.ai/papers/10.1002/1097-0258(20001130)19:22
- The paper
- Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine. https://doi.org/10.1002/1097-0258(20001130)19%3A22
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
