Cited by 1,952Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsStatistical Methods in Clinical Trials

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How Big is a Big Odds Ratio? Interpreting the Magnitudes of Odds Ratios in Epidemiological Studies

Chen, Henian, Cohen, Patricia, Chen, Sophie·2010·Communications in Statistics - Simulation and Computation

DOI 10.1080/03610911003650383

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Abstract

The odds ratio (OR) is probably the most widely used index of effect size in epidemiological studies. The difficulty of interpreting the OR has troubled many clinical researchers and epidemiologists for a long time. We propose a new method for interpreting the size of the OR by relating it to differences in a normal standard deviate. Our calculations indicate that OR = 1.68, 3.47, and 6.71 are equivalent to Cohen's d = 0.2 (small), 0.5 (medium), and 0.8 (large), respectively, when disease rate is 1% in the nonexposed group; Cohen's d < 0.2 when OR <1.5, and Cohen's d > 0.8 when OR > 5.

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10.1002/sim.1032 · DOI
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“[57] that equates Odds Ratio value cutoff scores to Cohen’s effect sizes (i.”
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“The cut-offs are OR of 1·68, 3·47 and 6·71 for small, medium and large effect size, respectively((81)).”
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“As the research question and the design were novel, the power calculation was based on a medium effect size (odds ratio ( OR ) = 3.47; Chen et al., 2010).”
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“Therefore, OR s were used as an effect size metric to demonstrate which comparisons resulted in a meaningful difference; small, medium, and large differences were OR > 1.68 (i.e., 90.5% adherence), 3.47 (i.e., 95.2% adherence), or 6.71 (i.e., 97% adherence), respectively (Chen et al., 2010).”
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“The binary logistic regression model yielded an odds ratio of 73.27 (95% Confidence Interval = 1.379, 3,895.046), which reflects a large effect size based on previously published interpretive guidelines (Chen et al., 2010).”
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“The first model summarized in Table 3 shows that the stronger an older man’s endorsement of hegemonic masculinity, the lower his odds of reporting he has any confidant (p = .002), representing a medium effect size (Chen et al. 2010) in support of Hypothesis 1.”
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“A conventional rule of thumb for interpretation is that an odds ratio equal to 1 indicates no difference/effect, while an odds ratio between 1.5 and 1.68 (and their 1 counterparts), is considered “small,” similar to Cohen’s D (Chen et al., 2010).”
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Authors: Chen, H., Cohen, P., & Chen, S.
Affiliations: Winthrop-University Hospital (US)
New York State Psychiatric Institute (US)
Health Canada (CA)
Corresponding author: Chen, Henian
ORCID: Chen, Henian
Chen, Sophie
Venue: Communications in Statistics - Simulation and Computation
Publisher: Informa UK Limited
Published: 2010-03-31
Volume: 39
Issue: 4
Pages: 860-864
ISSN: 0361-0918 (ISSN-L)
1532-4141
DOI: 10.1080/03610911003650383
OpenAlex: W2029059557
Cited by: 1,952
References: 20
Citations by year: 2026: 170 · 2025: 201 · 2024: 259 · 2023: 234 · 2022: 254 · 2021: 242 · 2020: 196 · 2019: 123 · 2018: 93 · 2017: 65 · 2016: 44 · 2015: 31 · 2014: 18 · 2013: 11 · 2012: 4
FWCI: 5.45
Type: journal article
Language: en
Open access: closed
Locations: Communications in Statistics - Simulation and Computation (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: odds ratio, effect size interpretation, Cohen's d, standardized mean difference, disease incidence

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How Big is a Big Odds Ratio? Interpreting the Magnitudes of Odds Ratios in Epidemiological Studies

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