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Cited by 437Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Bias in odds ratios by logistic regression modelling and sample size

Open access
Nemes, Szilard, Jonasson, Junmei Miao, Genell, Anna, Steineck, Gunnar·2009·BMC Medical Research Methodology
DOI 10.1186/1471-2288-9-56PMC PMC2724427
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Abstract

BACKGROUND: In epidemiological studies researchers use logistic regression as an analytical tool to study the association of a binary outcome to a set of possible exposures. METHODS: Using a simulation study we illustrate how the analytically derived bias of odds ratios modelling in logistic regression varies as a function of the sample size. RESULTS: Logistic regression overestimates odds ratios in studies with small to moderate samples size. The small sample size induced bias is a systematic one, bias away from null. Regression coefficient estimates shifts away from zero, odds ratios from one. CONCLUSION: If several small studies are pooled without consideration of the bias introduced by the inherent mathematical properties of the logistic regression model, researchers may be mislead to erroneous interpretation of the results.

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Figures

Figure 1 Coefficient estimates and its sample size dependent systematic bias in logistic regression estimates. The deviance from the true population value (2 respectively -0.9 in this case) represents the analytically induced bias in regression estimates.
Figure 1 Coefficient estimates and its sample size dependent systematic bias in logistic regression estimates. The deviance from the true population value (2 respectively -0.9 in this case) represents the analytically induced bias in regression estimates.CC BY · PubMed Central
Figure 2 Sampling distribution of logistic regression coefficient estimates at different sample sizes.
Figure 2 Sampling distribution of logistic regression coefficient estimates at different sample sizes.CC BY · PubMed Central
Figure 3 Increasing sample size not only reduces the analytically induced bias in regression estimates but protects against extreme value estimates.
Figure 3 Increasing sample size not only reduces the analytically induced bias in regression estimates but protects against extreme value estimates.CC BY · PubMed Central

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Metadata

Authors
Nemes, S., Jonasson, J. M., Genell, A., & Steineck, G.
Affiliations
  • University of Gothenburg (SE)
  • Karolinska Institutet (SE)
ORCID
Venue
BMC Medical Research Methodology
Publisher
Springer Science and Business Media LLC
Published
2009-07-27
Volume
9
Issue
1
Pages
56-56
ISSN
1471-2288 (ISSN-L)
DOI
10.1186/1471-2288-9-56
PMC
PMC2724427
OpenAlex
W2168885658
Cited by
437
References
18
Citations by year
2026: 14 · 2025: 29 · 2024: 41 · 2023: 26 · 2022: 33 · 2021: 32 · 2020: 35 · 2019: 27 · 2018: 24 · 2017: 17 · 2016: 27 · 2015: 31 · 2014: 21 · 2013: 24 · 2012: 30
FWCI
15.53
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Research Methodology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Cancerfonden
  • Vetenskapsrådet
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 › Advanced Causal Inference Techniques
Keywords
odds ratio bias, logistic regression, sample size, small sample bias, binary outcomes, simulation study
MeSH terms
Computer Simulation, Female, Humans, Pregnancy, Models, Statistical, Bias, Logistic Models, Odds Ratio, Sample Size

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