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Cited by 271Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsImbalanced Data Classification Techniques

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

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Austin, Peter C, Steyerberg, Ewout W·2012·BMC Medical Research Methodology
DOI 10.1186/1471-2288-12-82PMC PMC3528632
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Abstract

BACKGROUND: When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. METHODS: An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. RESULTS: Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. CONCLUSIONS: The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.

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Figure 1 Comparison of empirical and predicted c-statistics: normal distribution.
Figure 1 Comparison of empirical and predicted c-statistics: normal distribution.CC BY · PubMed Central
Figure 2 Difference between empirical and predicted c-statistic and skewness.
Figure 2 Difference between empirical and predicted c-statistic and skewness.CC BY · PubMed Central
Figure 3 Comparison of empirical and predicted c-statistics: non-normal distributions.
Figure 3 Comparison of empirical and predicted c-statistics: non-normal distributions.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Cohen J: Statistical Power Analysis for the Behavioural Sciences. 1988, Lawrence Erlbaum Associates, Hillsdale, NJ, 2
  2. Tu JV, Donovan LR, Lee DS, Austin PC, Ko DT, Wang JT, et al: Quality of Cardiac Care in Ontario. 2004, Institute for Clinical Evaluative Sciences, Toronto, Ontario

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Metadata

Authors
Austin, P. C., & Steyerberg, E. W.
Affiliations
  • Institute for Clinical Evaluative Sciences (CA)
  • Erasmus MC (NL)
Corresponding author
Austin, Peter C
ORCID
Venue
BMC Medical Research Methodology
Publisher
Springer Science and Business Media LLC
Published
2012-06-20
Volume
12
Issue
1
Pages
82-82
ISSN
1471-2288 (ISSN-L)
DOI
10.1186/1471-2288-12-82
PMC
PMC3528632
OpenAlex
W2112200626
Cited by
270
References
23
Citations by year
2026: 17 · 2025: 16 · 2024: 25 · 2023: 29 · 2022: 26 · 2021: 17 · 2020: 30 · 2019: 20 · 2018: 19 · 2017: 28 · 2016: 17 · 2015: 12 · 2014: 7 · 2013: 7
FWCI
7.93
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Research Methodology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (acceptedVersion, other-oa)
  • TSpace (University of Toronto) (submittedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Heart and Stroke Foundation of Canada
  • Institute for Clinical Evaluative Sciences
  • Ontario Ministry of Health and Long-Term Care
  • Canadian Institutes of Health Research (86508, MOP86508)
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 › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Keywords
area under the ROC curve, logistic regression, odds ratio, heteroscedasticity, prediction accuracy, discriminatory power, non-normal distributions, gamma distribution, lognormal distribution, uniform distribution
MeSH terms
Age Factors, Analysis of Variance, Data Interpretation, Statistical, Humans, Monte Carlo Method, Myocardial Infarction, ROC Curve, Logistic Models, Odds Ratio

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