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

Cadernos de Saúde Pública

Leonardo Soares Bastos¹  Raquel De Vasconcellos Carvalhaes De Oliveira¹  Luciane De Souza Velasque¹ 

关键词: Prevalence Ratio;    Logistic Models;    Cross-Sectional Studies;    Razão de Prevalências;    Modelos Logísticos;    Estudos Transversais;    Razón de Prevalencias;    Modelos Logísticos;    Estudios Transversales;   

DOI  :  10.1590/0102-311X00175413

来源: SciELO

【 摘 要 】

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.

【 授权许可】

CC BY   
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