The effect of misclassification error on risk estimation in case-control studies

Detalhes bibliográficos
Autor(a) principal: Baena,Armando
Data de Publicação: 2015
Outros Autores: Garcés-Palacio,Isabel Cristina, Grisales,Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista brasileira de epidemiologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2015000200341
Resumo: INTRODUCTION: In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. OBJECTIVE: To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to describe the trends in DME and NDME. METHODS: Different sensitivity levels, specificity levels, prevalence rates and odds ratios were simulated. Interaction graphics were constructed to study bias in the different settings, and the effect of the different factors on bias was described using linear models. RESULTS: One hundred per cent of the biases caused by NDME were negative. DME biased the association positively more often than it did negatively (70 versus 30%), increasing or decreasing the OR estimate towards the null hypothesis. CONCLUSIONS: The effect of the sensitivity and specificity in classifying exposure, the prevalence of exposure in controls and true OR differed between positive and negative biases. The use of valid exposure classification instruments with high sensitivity and high specificity is recommended to mitigate this type of bias.
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spelling The effect of misclassification error on risk estimation in case-control studiesClassificationBiasCase-control studiesOdds ratiosSensitivity and specificityComputer Simulation INTRODUCTION: In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. OBJECTIVE: To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to describe the trends in DME and NDME. METHODS: Different sensitivity levels, specificity levels, prevalence rates and odds ratios were simulated. Interaction graphics were constructed to study bias in the different settings, and the effect of the different factors on bias was described using linear models. RESULTS: One hundred per cent of the biases caused by NDME were negative. DME biased the association positively more often than it did negatively (70 versus 30%), increasing or decreasing the OR estimate towards the null hypothesis. CONCLUSIONS: The effect of the sensitivity and specificity in classifying exposure, the prevalence of exposure in controls and true OR differed between positive and negative biases. The use of valid exposure classification instruments with high sensitivity and high specificity is recommended to mitigate this type of bias. Associação Brasileira de Saúde Coletiva2015-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2015000200341Revista Brasileira de Epidemiologia v.18 n.2 2015reponame:Revista brasileira de epidemiologia (Online)instname:Associação Brasileira de Saúde Coletiva (ABRASCO)instacron:ABRASCO10.1590/1980-5497201500020005info:eu-repo/semantics/openAccessBaena,ArmandoGarcés-Palacio,Isabel CristinaGrisales,Hugoeng2015-06-09T00:00:00Zoai:scielo:S1415-790X2015000200341Revistahttp://www.scielo.br/rbepidhttps://old.scielo.br/oai/scielo-oai.php||revbrepi@usp.br1980-54971415-790Xopendoar:2015-06-09T00:00Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)false
dc.title.none.fl_str_mv The effect of misclassification error on risk estimation in case-control studies
title The effect of misclassification error on risk estimation in case-control studies
spellingShingle The effect of misclassification error on risk estimation in case-control studies
Baena,Armando
Classification
Bias
Case-control studies
Odds ratios
Sensitivity and specificity
Computer Simulation
title_short The effect of misclassification error on risk estimation in case-control studies
title_full The effect of misclassification error on risk estimation in case-control studies
title_fullStr The effect of misclassification error on risk estimation in case-control studies
title_full_unstemmed The effect of misclassification error on risk estimation in case-control studies
title_sort The effect of misclassification error on risk estimation in case-control studies
author Baena,Armando
author_facet Baena,Armando
Garcés-Palacio,Isabel Cristina
Grisales,Hugo
author_role author
author2 Garcés-Palacio,Isabel Cristina
Grisales,Hugo
author2_role author
author
dc.contributor.author.fl_str_mv Baena,Armando
Garcés-Palacio,Isabel Cristina
Grisales,Hugo
dc.subject.por.fl_str_mv Classification
Bias
Case-control studies
Odds ratios
Sensitivity and specificity
Computer Simulation
topic Classification
Bias
Case-control studies
Odds ratios
Sensitivity and specificity
Computer Simulation
description INTRODUCTION: In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. OBJECTIVE: To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to describe the trends in DME and NDME. METHODS: Different sensitivity levels, specificity levels, prevalence rates and odds ratios were simulated. Interaction graphics were constructed to study bias in the different settings, and the effect of the different factors on bias was described using linear models. RESULTS: One hundred per cent of the biases caused by NDME were negative. DME biased the association positively more often than it did negatively (70 versus 30%), increasing or decreasing the OR estimate towards the null hypothesis. CONCLUSIONS: The effect of the sensitivity and specificity in classifying exposure, the prevalence of exposure in controls and true OR differed between positive and negative biases. The use of valid exposure classification instruments with high sensitivity and high specificity is recommended to mitigate this type of bias.
publishDate 2015
dc.date.none.fl_str_mv 2015-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2015000200341
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2015000200341
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1980-5497201500020005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Saúde Coletiva
publisher.none.fl_str_mv Associação Brasileira de Saúde Coletiva
dc.source.none.fl_str_mv Revista Brasileira de Epidemiologia v.18 n.2 2015
reponame:Revista brasileira de epidemiologia (Online)
instname:Associação Brasileira de Saúde Coletiva (ABRASCO)
instacron:ABRASCO
instname_str Associação Brasileira de Saúde Coletiva (ABRASCO)
instacron_str ABRASCO
institution ABRASCO
reponame_str Revista brasileira de epidemiologia (Online)
collection Revista brasileira de epidemiologia (Online)
repository.name.fl_str_mv Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)
repository.mail.fl_str_mv ||revbrepi@usp.br
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