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

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