2021
DOI: 10.1590/0001-3765202120190316
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Prevalence ratio estimation via logistic regression: a tool in R

Abstract: The interpretation of odds ratios (OR) as prevalence ratios (PR) in cross-sectional studies have been criticized since this equivalence is not true unless under specific circumstances. The logistic regression model is a very well known statistical tool for analysis of binary outcomes and frequently used to obtain adjusted OR. Here, we introduce the prLogistic for the R statistical computing environment which can be obtained from The Comprehensive R Archive Network, https://cran.r-project.org/ package=prLogisti… Show more

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Cited by 2 publications
(2 citation statements)
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“…The variables with a p-value ≤0.05 or defined as relevant by their magnitude in the bivariate analysis were included in the final models. In the evaluation of the predictors of PrEP initiation, we fitted independent models yielding prevalence ratios (PR) and respective 95% confidence intervals (CI) using logistic regression models and the delta method for CI estimation [ 47 ]. Multicollinearity was analyzed using association tests between selected covariates for the models, and the adequacy of the final models was analyzed using the Hosmer-Lemeshow goodness-of-fit test [ 48 ], considering a cutoff p value of 0.05.…”
Section: Methodsmentioning
confidence: 99%
“…The variables with a p-value ≤0.05 or defined as relevant by their magnitude in the bivariate analysis were included in the final models. In the evaluation of the predictors of PrEP initiation, we fitted independent models yielding prevalence ratios (PR) and respective 95% confidence intervals (CI) using logistic regression models and the delta method for CI estimation [ 47 ]. Multicollinearity was analyzed using association tests between selected covariates for the models, and the adequacy of the final models was analyzed using the Hosmer-Lemeshow goodness-of-fit test [ 48 ], considering a cutoff p value of 0.05.…”
Section: Methodsmentioning
confidence: 99%
“…Sensitivity analyses aimed to elucidate the relationships between childhood developmental outcomes and specific combinations of parental mental disorders diagnosed at any time during the study period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) to maximise statistical power. Prevalence ratios (PRs) were derived via conditional standardisation of regression coefficients with 95% confidence Intervals (CIs) estimated using the delta method (Amorim and Ospina, 2021;Bieler et al, 2010). PRs between 1.00 and 1.49 (or 1.00 and 0.67) were considered small, 1.50 and 2.49 (or 0.66 and 0.40) as medium and 2.50 and over (or <0.40) as large (Rosenthal, 1996).…”
Section: Statistical Analysesmentioning
confidence: 99%