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Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression

Michael E Reichenheim1* and Evandro SF Coutinho12

Author Affiliations

1 Department of Epidemiology, Institute of Social Medicine (IMS), Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil

2 Department of Epidemiology and Quantitative Methods, National School of Public Health Sergio Arouca (ENSP), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil

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BMC Medical Research Methodology 2010, 10:66  doi:10.1186/1471-2288-10-66

Published: 15 July 2010



Several papers have discussed which effect measures are appropriate to capture the contrast between exposure groups in cross-sectional studies, and which related multivariate models are suitable. Although some have favored the Prevalence Ratio over the Prevalence Odds Ratio -- thus suggesting the use of log-binomial or robust Poisson instead of the logistic regression models -- this debate is still far from settled and requires close scrutiny.


In order to evaluate how accurately true causal parameters such as Incidence Density Ratio (IDR) or the Cumulative Incidence Ratio (CIR) are effectively estimated, this paper presents a series of scenarios in which a researcher happens to find a preset ratio of prevalences in a given cross-sectional study. Results show that, provided essential and non-waivable conditions for causal inference are met, the CIR is most often inestimable whether through the Prevalence Ratio or the Prevalence Odds Ratio, and that the latter is the measure that consistently yields an appropriate measure of the Incidence Density Ratio.


Multivariate regression models should be avoided when assumptions for causal inference from cross-sectional data do not hold. Nevertheless, if these assumptions are met, it is the logistic regression model that is best suited for this task as it provides a suitable estimate of the Incidence Density Ratio.