title: Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies.
abstract: In the last decades, the use of the epidemiological prevalence ratio (PR) instead of the odds ratio has been debated as a measure of association in cross-sectional studies. This article addresses the main difficulties in the use of statistical models for the calculation of PR: convergence problems, availability of tools and inappropriate assumptions. We implement the direct approach to estimate the PR from binary regression models based on two methods proposed by Wilcosky & Chambless and compare with different methods. We used three examples and compared the crude and adjusted estimate of PR, with the estimates obtained by use of log-binomial, Poisson regression and the prevalence odds ratio (POR). PRs obtained from the direct approach resulted in values close enough to those obtained by log-binomial and Poisson, while the POR overestimated the PR. The model implemented here showed the following advantages: no numerical instability; assumes adequate probability distribution and, is available through the R statistical package.
authors: Leonardo Soares
authors: LS
authors: Bastos
authors: Raquel de Vasconcellos Carvalhaes de
authors: Rde V
authors: Oliveira
authors: Luciane de Souza
authors: Lde S
authors: Velasque
mesh_terms: D003430:Cross-Sectional Studies / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D016017:Odds Ratio; D015995:Prevalence; D011336:Probability