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

<p>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 p...

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Main Authors: Leonardo Soares Bastos, Raquel de Vasconcellos Carvalhaes de Oliveira, Luciane de Souza Velasque
Format: Article
Language:English
Published: Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz 2015-03-01
Series:Cadernos de Saúde Pública
Subjects:
Online Access:http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0102-311X2015000300487&lng=en&tlng=en
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author Leonardo Soares Bastos
Raquel de Vasconcellos Carvalhaes de Oliveira
Luciane de Souza Velasque
author_facet Leonardo Soares Bastos
Raquel de Vasconcellos Carvalhaes de Oliveira
Luciane de Souza Velasque
author_sort Leonardo Soares Bastos
collection DOAJ
description <p>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.</p>
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spelling doaj.art-c0c27ba3ddd14b9b8cd64e7369fe52612022-12-21T19:06:24ZengEscola Nacional de Saúde Pública, Fundação Oswaldo CruzCadernos de Saúde Pública0102-311X2015-03-0131348749510.1590/0102-311X00175413S0102-311X2015000300487Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studiesLeonardo Soares BastosRaquel de Vasconcellos Carvalhaes de OliveiraLuciane de Souza Velasque<p>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.</p>http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0102-311X2015000300487&lng=en&tlng=enRazón de PrevalenciasModelos LogísticosEstudios Transversales
spellingShingle Leonardo Soares Bastos
Raquel de Vasconcellos Carvalhaes de Oliveira
Luciane de Souza Velasque
Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
Cadernos de Saúde Pública
Razón de Prevalencias
Modelos Logísticos
Estudios Transversales
title Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
title_full Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
title_fullStr Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
title_full_unstemmed Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
title_short Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies
title_sort obtaining adjusted prevalence ratios from logistic regression models in cross sectional studies
topic Razón de Prevalencias
Modelos Logísticos
Estudios Transversales
url http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0102-311X2015000300487&lng=en&tlng=en
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AT lucianedesouzavelasque obtainingadjustedprevalenceratiosfromlogisticregressionmodelsincrosssectionalstudies