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Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling

The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (>10%), the bia...

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Autores principales: Gnardellis, Charalambos, Notara, Venetia, Papadakaki, Maria, Gialamas, Vasilis, Chliaoutakis, Joannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689401/
https://www.ncbi.nlm.nih.gov/pubmed/36428910
http://dx.doi.org/10.3390/diagnostics12112851
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author Gnardellis, Charalambos
Notara, Venetia
Papadakaki, Maria
Gialamas, Vasilis
Chliaoutakis, Joannes
author_facet Gnardellis, Charalambos
Notara, Venetia
Papadakaki, Maria
Gialamas, Vasilis
Chliaoutakis, Joannes
author_sort Gnardellis, Charalambos
collection PubMed
description The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (>10%), the bias in assessing the relative risk may be very high. Meta-analysis studies have estimated that about 40% of the relative risk estimates in prospective investigations, through binary logistic models, lead to extensive bias of the population parameters. The problem of risk inflation also appears in cross-sectional studies with binary outcomes, where the parameter of interest is the prevalence ratio. As an alternative to the use of logistic regression models in both longitudinal and cross-sectional studies, the modified Poisson regression model is proposed.
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spelling pubmed-96894012022-11-25 Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling Gnardellis, Charalambos Notara, Venetia Papadakaki, Maria Gialamas, Vasilis Chliaoutakis, Joannes Diagnostics (Basel) Article The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (>10%), the bias in assessing the relative risk may be very high. Meta-analysis studies have estimated that about 40% of the relative risk estimates in prospective investigations, through binary logistic models, lead to extensive bias of the population parameters. The problem of risk inflation also appears in cross-sectional studies with binary outcomes, where the parameter of interest is the prevalence ratio. As an alternative to the use of logistic regression models in both longitudinal and cross-sectional studies, the modified Poisson regression model is proposed. MDPI 2022-11-17 /pmc/articles/PMC9689401/ /pubmed/36428910 http://dx.doi.org/10.3390/diagnostics12112851 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gnardellis, Charalambos
Notara, Venetia
Papadakaki, Maria
Gialamas, Vasilis
Chliaoutakis, Joannes
Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title_full Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title_fullStr Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title_full_unstemmed Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title_short Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
title_sort overestimation of relative risk and prevalence ratio: misuse of logistic modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689401/
https://www.ncbi.nlm.nih.gov/pubmed/36428910
http://dx.doi.org/10.3390/diagnostics12112851
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