Odds ratios and logistic regression: further examples of their use and interpretation
Susan M. Hailpern, MS, MPH
School of Public Health
New York Medical College
Valhalla, NY
|
Paul F. Visintainer, PhD
School of Public Health
New York Medical College
Valhalla, NY
|
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 2×2 table and discuss its
application to both cohort and case–control study design.
Interpretations of odds ratios, relative risk, and Β0 from
the logit model are presented.
View all articles by these authors:
Susan M. Hailpern, MS, MPH, Paul F. Visintainer, PhD
View all articles with these keywords:
cc, cci, cs, csi, logistic, logit, relative risk, case–control study, odds ratio, cohort study
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
