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 accessAbstract
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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“using maximum likelihood estimation, as is done for logistic regression and Cox proportional hazards regression for time to event outcomes, yields only asymptotically unbiased estimates of individual coefficients [23].”
From A calibration hierarchy for risk models was defined: from utopia to empirical data (2016) · cites this paper as [23]
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A calibration hierarchy for risk models was defined: from utopia to empirical data
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.005 · Cited by 910
Sample size for binary logistic prediction models: Beyond events per variable criteria
van Smeden, M., Moons, K. G. M., de Groot, J. A. H., Collins, P. G. S., Altman, D. G., Eijkemans, M. J. C., & Reitsma, J. B. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280218784726 · Cited by 820
Bujang, M., Sa'at, N., Sidik, T. M. I. T. A. B., & Joo, L. C. (2018) · Malaysian Journal of Medical Sciences · doi:10.21315/mjms2018.25.4.12 · Cited by 661
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
van Smeden, M., de Groot, J. A. H., Moons, K. G. M., Collins, P. G. S., Altman, D. G., Eijkemans, M. J. C., & Reitsma, J. B. (2016) · BMC Medical Research Methodology · doi:10.1186/s12874-016-0267-3 · Cited by 613
Strategies for multiple imputation in longitudinal studies.
Spratt, M., Carpenter, J., Sterne, J., Carlin, J., Heron, J., Henderson, J., & Tilling, K. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq137 · Cited by 395
Metadata
- Authors
- Nemes, S., Jonasson, J. M., Genell, A., & Steineck, G.
- Affiliations
- University of Gothenburg (SE)
- Karolinska Institutet (SE)
- Venue
- BMC Medical Research Methodology
- Published
- 2009-07-27
- Volume
- 9
- Issue
- 1
- Pages
- 56-56
- ISSN
- 1471-2288 (ISSN-L)
- 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
- gold
- doi.org
- 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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