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Odds ratios and logistic regression: further examples of their use and interpretation

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Listed:
  • Susan M. Hailpern

    (School of Public Health, New York Medical College)

  • Paul F. Visintainer

    (School of Public Health, New York Medical College)

Abstract

Logistic regression is perhaps the most widely used method for adjustment of confounding in epidemiologic studies. Its popularity is understandable. The method can simultaneously adjust for confounders measured on different scales; it provides estimates that are clinically interpretable; and its estimates are valid in a variety of study designs with few underlying assumptions. To those of us in practice settings, several aspects of applying and interpreting the model, however, can be confusing and counterintuitive. We attempt to clarify some of these points through several examples. We apply the method to a study of risk factors associated with periventricular leucomalacia and intraventricular hemorrhage in neonates. We relate the logit model to Cornfield's 2x2 table and discuss its application to both cohort and case-control study design. Interpretations of odds ratios, relative risk, and beta_0 from the logit model are presented. Copyright 2003 by StataCorp LP.

Suggested Citation

  • Susan M. Hailpern & Paul F. Visintainer, 2003. "Odds ratios and logistic regression: further examples of their use and interpretation," Stata Journal, StataCorp LP, vol. 3(3), pages 213-225, September.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:3:p:213-225
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    References listed on IDEAS

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    1. William Gould, 2000. "Interpreting logistic regression in all its forms," Stata Technical Bulletin, StataCorp LP, vol. 9(53).
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