CoChat
Sign Up
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1471-2288-9-56

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 18 references

Cited by

Sorted by citations

Items 101–200 of 436

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review