Prevalence ratio estimation via logistic regression: a tool in R

Detalhes bibliográficos
Autor(a) principal: AMORIM,LEILA D.
Data de Publicação: 2021
Outros Autores: OSPINA,RAYDONAL
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700301
Resumo: 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=prLogistic. The package prLogistic was built to assist the estimation of PR via logistic regression models adjusted by delta method and bootstrap for analysis of independent and correlated binary data. Two applications are presented to illustrate its use for analysis of independent observations and data from clustered studies.
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spelling Prevalence ratio estimation via logistic regression: a tool in RLogistic modeldelta methodbootstrapprevalence ratiosAbstract 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=prLogistic. The package prLogistic was built to assist the estimation of PR via logistic regression models adjusted by delta method and bootstrap for analysis of independent and correlated binary data. Two applications are presented to illustrate its use for analysis of independent observations and data from clustered studies.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700301Anais da Academia Brasileira de Ciências v.93 n.4 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120190316info:eu-repo/semantics/openAccessAMORIM,LEILA D.OSPINA,RAYDONALeng2021-09-20T00:00:00Zoai:scielo:S0001-37652021000700301Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-09-20T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Prevalence ratio estimation via logistic regression: a tool in R
title Prevalence ratio estimation via logistic regression: a tool in R
spellingShingle Prevalence ratio estimation via logistic regression: a tool in R
AMORIM,LEILA D.
Logistic model
delta method
bootstrap
prevalence ratios
title_short Prevalence ratio estimation via logistic regression: a tool in R
title_full Prevalence ratio estimation via logistic regression: a tool in R
title_fullStr Prevalence ratio estimation via logistic regression: a tool in R
title_full_unstemmed Prevalence ratio estimation via logistic regression: a tool in R
title_sort Prevalence ratio estimation via logistic regression: a tool in R
author AMORIM,LEILA D.
author_facet AMORIM,LEILA D.
OSPINA,RAYDONAL
author_role author
author2 OSPINA,RAYDONAL
author2_role author
dc.contributor.author.fl_str_mv AMORIM,LEILA D.
OSPINA,RAYDONAL
dc.subject.por.fl_str_mv Logistic model
delta method
bootstrap
prevalence ratios
topic Logistic model
delta method
bootstrap
prevalence ratios
description 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=prLogistic. The package prLogistic was built to assist the estimation of PR via logistic regression models adjusted by delta method and bootstrap for analysis of independent and correlated binary data. Two applications are presented to illustrate its use for analysis of independent observations and data from clustered studies.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700301
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765202120190316
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.93 n.4 2021
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