Odds Ratio: Review about the Meaning of an Epidemiological Measure

Pedro Aguiar, Baltazar Nunes

Abstract


Introduction: It is very important to review the meaning of the Odds Ratio as a measure of effect and association, as well as, the bias of the Odds Ratio when it is assumed as a risk ratio or a prevalence ratio in the case of frequent disease or frequent health outcome.
Material and Methods: We simulated in a cohort of 200 individuals with 100 exposed and 100 non-exposed to a risk factor, a first setting of rare disease and a second setting of a more frequent disease. In both settings the risk ratios were similar. We computed the Odds Ratio and Relative Risks by the classical approach (standard method) and respectively by logistic regression and Poisson regression. After these, we introduced in the cohort a confounding variable and then we computed the Odds Ratio and Relative Risk by Mantel-Hanszel stratified analysis (standard method) and respectively by multiple logistic regression and multiple Poisson regression.
We used the 95% confidence interval in parameter estimation and SPSS V20 was used in statistical analysis.
Results: In the case of rare disease the Odds Ratio was very close to the Relative Risk. For more frequent disease the Odds Ratio overestimated the Relative Risk. In this situation and with a confounding variable, the relative Risk adjusted by Poisson regression was more valid then the Odds Ratio to represent a risk ratio. The confidence intervals of the Relative Risk adjusted by Poisson regression were always greater than Mantel-Hanszel confidence intervals.
Conclusions: The Odds Ratio and multiple logistic regression were valid analytic procedures in several epidemiological designs such as case-control studies and exploratory prospective studies as well as exploratory cross-sectional studies. The Odds Ratio should not be interpreted as a risk ratio or a prevalence ratio in the case of a health outcome that it is not rare. The multiple Poisson regression should be considered as an alternative procedure to logistic regression, especially if we want to estimate the effect of a specific exposure to a risk factor.

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