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

Chen, Henian; Cohen, Patricia; Chen, Sophie

doi:10.1080/03610911003650383

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

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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From The psychological subtype of intimate partner violence and its effect on mental health: a systematic review with meta-analyses (2022) · cites this paper as [91]
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From Race and Gender Differences in Undergraduate Research Mentoring Structures and Research Outcomes (2017) · cites this paper as Chen et al., 2010

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“As reported in Table 5, the Cohen’s d equivalent for the changes in the OR for African American and Latinx students relative to European American students was medium to large (Chen et al., 2010; Yuanyuan Lu and Henian Chen, personal communication, November 26, 2018).”
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“Furthermore, we observed medium-to-large effect sizes implying that WM updating deficits were potent antecedents of late-life GAD (average OR estimates were 6.05–6.31 herein) (Chen et al. 2010).”
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“In bivariate analyses, effect sizes of differences between the SI groups on demographic and military variables were small to moderate in magnitude (Table 2; Chen et al., 2010; Cohen, 1992).”
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“For OR (log OR) values of 1.6 (0.47), 3.47 (1.24), and 6.71 (1.90) were interpreted as small, moderate and large effects (Chen et al., 2010).”
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“Results are presented for demographic variables as AOR, holding all other variables constant, and demonstrate mostly weak to weakly moderate effect sizes (Chen et al., 2010).”
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“However, the media and neighbour-related factors demonstrated only small effects in the model due to their small odds ratios (OR 1.68 and .60; (Chen, Cohen, and Chen 2010)), which may suggest their effect is negligible or unlikely to be replicated with a sample of this size.”
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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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