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The importance of assessing the fit of logistic regression models: A case study

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  • Hosmer, D.W.
  • Taber, S.
  • Lemeshow, S.

Abstract

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.

Suggested Citation

  • Hosmer, D.W. & Taber, S. & Lemeshow, S., 1991. "The importance of assessing the fit of logistic regression models: A case study," American Journal of Public Health, American Public Health Association, vol. 81(12), pages 1630-1635.
    • Handle: RePEc:aph:ajpbhl:1991:81:12:1630-1635_3
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    Cited by:

    1. Ludwing Flórez-Salamanca & Edwin Herazo & Heidi Celina Oviedo & Adalberto Campo-Arias, 2014. "Prevalence and Predictors of High Sexual Prejudice Among Medical Students From Two Colombian Cities," SAGE Open, , vol. 4(2), pages 21582440145, June.
    2. Zhongheng Zhang & Hongying Ni, 2015. "Prediction Model for Critically Ill Patients with Acute Respiratory Distress Syndrome," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    3. R.P.G. Amithani & D.S. Rodrigo & C.L. Jayasinghe, 2021. "Local Factors Affecting Fertility of Women in Sri Lanka," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(6), pages 121-129, June.
    4. Ostrovski, Vladimir, 2022. "Testing equivalence to binary generalized linear models with application to logistic regression," Statistics & Probability Letters, Elsevier, vol. 191(C).
    5. Rino Bellocco & Sara Algeri, 2011. "Goodness-of-fit tests for categorical data: Comparing Stata, R, and SAS," Italian Stata Users' Group Meetings 2011 03, Stata Users Group.
    6. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2016. "Generalized Information Matrix Tests for Detecting Model Misspecification," Econometrics, MDPI, vol. 4(4), pages 1-24, November.

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