Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cadernos de Saúde Pública |
Texto Completo: | https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794 |
Resumo: | This article presents alternatives for modeling body mass index (BMI) as a continuous variable and the role of residual analysis. We sought strategies for the application of generalized linear models with appropriate statistical adjustment and easy interpretation of results. The analysis included 2,060 participants in Phase 1 of a longitudinal study (Pró-Saúde Study) with complete data on weight, height, age, race, family income, and schooling. In our study, the residual analysis of models estimated by maximum likelihood methods yielded inadequate adjustment. The transformed response variable resulted in a good fit but did not lead to estimates with straightforward interpretation. The best alternative was to apply quasi-likelihood as the estimation method, presenting a better adjustment and constant variance. In epidemiological data modeling, researchers should always take trade-offs into account between adequate statistical techniques and interpretability of results. |
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Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysisBody Mass IndexLinear ModelsStatistical Data InterpretationThis article presents alternatives for modeling body mass index (BMI) as a continuous variable and the role of residual analysis. We sought strategies for the application of generalized linear models with appropriate statistical adjustment and easy interpretation of results. The analysis included 2,060 participants in Phase 1 of a longitudinal study (Pró-Saúde Study) with complete data on weight, height, age, race, family income, and schooling. In our study, the residual analysis of models estimated by maximum likelihood methods yielded inadequate adjustment. The transformed response variable resulted in a good fit but did not lead to estimates with straightforward interpretation. The best alternative was to apply quasi-likelihood as the estimation method, presenting a better adjustment and constant variance. In epidemiological data modeling, researchers should always take trade-offs into account between adequate statistical techniques and interpretability of results.Neste artigo, discutem-se alternativas de modelagem do índice de massa corporal (IMC), analisado como variável contínua, e a análise de resíduos. Buscaram-se estratégias de aplicação dos modelos lineares generalizados adequadas tanto do ponto de vista do ajuste estatístico quanto da facilidade de interpretação dos resultados. Nestas análises, foram incluídos dados relativos a 2.060 participantes da Fase 1 de estudo longitudinal (Estudo Pró-Saúde), com informação completa de peso, estatura, idade, raça/cor, renda familiar e escolaridade. Em nosso estudo, a análise de resíduos dos modelos estimados pelo método da máxima verossimilhança, amplamente utilizado, não possibilitou ajuste adequado dos modelos aos dados. A transformação da variável resposta, apesar de resultar em um bom ajuste, não conduziu a estimativas de fácil interpretação. Considerou-se como melhor alternativa a mudança do método de estimação para quase-verossimilhança. Assim, melhor ajuste foi alcançado e a variância permaneceu constante. Na modelagem de dados epidemiológicos, cabe aos pesquisadores buscarem o melhor equilíbrio entre a aplicação adequada de técnicas estatísticas e a facilidade de interpretação dos dados.Reports in Public HealthCadernos de Saúde Pública2008-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794Reports in Public Health; Vol. 24 No. 2 (2008): FebruaryCadernos de Saúde Pública; v. 24 n. 2 (2008): Fevereiro1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794/7697https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794/7698Fonseca, Maria de Jesus Mendes daAndreozzi, Valeska LimaFaerstein, EduardoChor, DoraCarvalho, Marília Sáinfo:eu-repo/semantics/openAccess2024-03-06T15:27:47Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/3794Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:04:21.520498Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true |
dc.title.none.fl_str_mv |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
title |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
spellingShingle |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis Fonseca, Maria de Jesus Mendes da Body Mass Index Linear Models Statistical Data Interpretation |
title_short |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
title_full |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
title_fullStr |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
title_full_unstemmed |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
title_sort |
Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis |
author |
Fonseca, Maria de Jesus Mendes da |
author_facet |
Fonseca, Maria de Jesus Mendes da Andreozzi, Valeska Lima Faerstein, Eduardo Chor, Dora Carvalho, Marília Sá |
author_role |
author |
author2 |
Andreozzi, Valeska Lima Faerstein, Eduardo Chor, Dora Carvalho, Marília Sá |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Fonseca, Maria de Jesus Mendes da Andreozzi, Valeska Lima Faerstein, Eduardo Chor, Dora Carvalho, Marília Sá |
dc.subject.por.fl_str_mv |
Body Mass Index Linear Models Statistical Data Interpretation |
topic |
Body Mass Index Linear Models Statistical Data Interpretation |
description |
This article presents alternatives for modeling body mass index (BMI) as a continuous variable and the role of residual analysis. We sought strategies for the application of generalized linear models with appropriate statistical adjustment and easy interpretation of results. The analysis included 2,060 participants in Phase 1 of a longitudinal study (Pró-Saúde Study) with complete data on weight, height, age, race, family income, and schooling. In our study, the residual analysis of models estimated by maximum likelihood methods yielded inadequate adjustment. The transformed response variable resulted in a good fit but did not lead to estimates with straightforward interpretation. The best alternative was to apply quasi-likelihood as the estimation method, presenting a better adjustment and constant variance. In epidemiological data modeling, researchers should always take trade-offs into account between adequate statistical techniques and interpretability of results. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-02-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794 |
url |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794/7697 https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/3794/7698 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html application/pdf |
dc.publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
dc.source.none.fl_str_mv |
Reports in Public Health; Vol. 24 No. 2 (2008): February Cadernos de Saúde Pública; v. 24 n. 2 (2008): Fevereiro 1678-4464 0102-311X reponame:Cadernos de Saúde Pública instname:Fundação Oswaldo Cruz (FIOCRUZ) instacron:FIOCRUZ |
instname_str |
Fundação Oswaldo Cruz (FIOCRUZ) |
instacron_str |
FIOCRUZ |
institution |
FIOCRUZ |
reponame_str |
Cadernos de Saúde Pública |
collection |
Cadernos de Saúde Pública |
repository.name.fl_str_mv |
Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ) |
repository.mail.fl_str_mv |
cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br |
_version_ |
1798943363538354176 |