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

Dados Bibliográficos

ID5227089
AutoresLeonardo Soares Bastos (0000-0002-1406-0122, Fundação Oswaldo Cruz), Raquel De Vasconcellos Carvalhaes De Oliveira (0000-0001-9387-8645, Fundação Oswaldo Cruz), Luciane De Souza Velásquez, Luciane Velasque (0000-0002-4269-4755, Universidade Federal do Estado do Rio de Janeiro), Luciane de Souza Velasque (Universidade Federal do Estado do Rio de Janeiro)
Ano2015
Volume31
Fascículo3
Páginas487-495
Data de publicação2015-03-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCadernos de Saude Publica (JOURNAL)
Identificadores do periódicoISSN: 0102-311X • E-ISSN: 1678-4464
EditoraFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/0102-311x00175413
PMID25859716
OpenAlexW2148106249
SCIELO_PIDS0102-311X2015000300487
IdiomaEN
Citações recebidas15
Referências citadas19

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

Binomial distribution · Binomial regression · Econometrics · Environmental health · Generalized linear model · Logistic regression · Negative binomial distribution · Odds · Odds ratio · Poisson distribution · Poisson regression · Population · Regression analysis · Statistics · Advanced Causal Inference Techniques · Food Security and Health in Diverse Populations · Mathematics · Medicine · Statistical Methods and Bayesian Inference