Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies

• Autor(a) principal: Leonardo Soares Bastos
• Data de Publicação: 2015
• Outros Autores: Raquel de Vasconcellos Carvalhaes de Oliveira, Luciane de Souza Velasque
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Cadernos de Saúde Pública
• Texto Completo: https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/5978
• topic: Prevalence Ratio; Logistic Models; Cross-Sectional Studies

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.