The importance of assessing the fit of logistic regression models: a case study.
Open accessAbstract
BACKGROUND: The logistic regression model is being used with increasing frequency in all areas of public health research. In the calendar year 1989, over 30% of the articles published in the American Journal of Public Health employed some form of logistic regression modeling. In spite of this increase, there has been no commensurate increase in the use of commonly available methods for assessing model adequacy. METHODS: We review the current status of the use of logistic regression modeling in the American Journal of Public Health. We present a brief overview of currently available and easily used methods for assessing the adequacy of a fitted logistic regression model. RESULTS: An example is used to demonstrate the methods as well as a few of the adverse consequences of failing to assess the fit of the model. One important adverse consequence illustrated in the example is the inclusion of variables in the model as a result of the influence of one subject. CONCLUSIONS: Failure to address model adequacy may lead to misleading or incorrect inferences. Recommendations are made for the use of methods for assessing model adequacy and for future editorial policy in regard to the review of articles using logistic regression.
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Widely used statistical models, including logistic and proportional hazards (Cox) regression, are based on ratio measures.
“Widely used statistical models, including logistic [ 169 ] and proportional hazards (Cox) regression [ 170 ] are based on ratio measures.”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [169]
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- Authors
- Hosmer, D. W., Taber, S., & Lemeshow, S.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 1991-12
- Volume
- 81
- Issue
- 12
- Pages
- 1630-1635
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC1405276
- OpenAlex
- W1984363050
- Cited by
- 496
- References
- 7
- Citations by year
- 2026: 11 · 2025: 7 · 2024: 18 · 2023: 9 · 2022: 16 · 2021: 19 · 2020: 15 · 2019: 17 · 2018: 8 · 2017: 14 · 2016: 13 · 2015: 21 · 2014: 14 · 2013: 28 · 2012: 27
- FWCI
- 6.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- logistic regression, model fit assessment, model adequacy, influential observations, editorial policy, statistical reporting quality, public health research
- MeSH terms
- Data Interpretation, Statistical, Evaluation Studies as Topic, Humans, Periodicals as Topic, Public Health, Publishing, Reproducibility of Results, Bias, Logistic Models
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.
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