Regression models for binary response applied to data on neonatal deaths in newborns
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/45642 |
Resumo: | The present study presents binary data modeling regarding 1.6% of neonatal deaths in 3,448 newborns from an epidemiological and observational study with a cross-sectional design, involving the retrospective analysis of 4,293 medical records of high-risk pregnant women followed in a gestational outpatient clinic from September 2012 to September 2017. Different symmetric and asymmetric link functions were considered by means of Bayesian inference. The support of more accurate inferences regarding the parameters of the model will provide biological interpretations that are more reliable and consistent with the reality. The model that presented, significantly, the lowest value for the deviance information criterion (DIC = 398.8), was the binomial with power logit (PL) link function, whose median posterior value estimated and significant for the parameter asymmetry was l = 0.25 (0.14;1.17). This significance is observed in all other models of the power family, however with very different values and significantly higher DIC values, indicating less parsimonious models. The Bayesian methodology proved to be flexible. Additionally, the results show that such model shows an accuracy = 97.4% and area under the ROC curve AUC = 89.4% in the prediction of neonatal deaths based on the weight of children at birth. Specifically, for 2.500g, a value predicted in the medical literature for low weight, the model predicts a probability of 1.43%. |
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Regression models for binary response applied to data on neonatal deaths in newbornsasymmetric link; Bayesian inference; power link.The present study presents binary data modeling regarding 1.6% of neonatal deaths in 3,448 newborns from an epidemiological and observational study with a cross-sectional design, involving the retrospective analysis of 4,293 medical records of high-risk pregnant women followed in a gestational outpatient clinic from September 2012 to September 2017. Different symmetric and asymmetric link functions were considered by means of Bayesian inference. The support of more accurate inferences regarding the parameters of the model will provide biological interpretations that are more reliable and consistent with the reality. The model that presented, significantly, the lowest value for the deviance information criterion (DIC = 398.8), was the binomial with power logit (PL) link function, whose median posterior value estimated and significant for the parameter asymmetry was l = 0.25 (0.14;1.17). This significance is observed in all other models of the power family, however with very different values and significantly higher DIC values, indicating less parsimonious models. The Bayesian methodology proved to be flexible. Additionally, the results show that such model shows an accuracy = 97.4% and area under the ROC curve AUC = 89.4% in the prediction of neonatal deaths based on the weight of children at birth. Specifically, for 2.500g, a value predicted in the medical literature for low weight, the model predicts a probability of 1.43%.Universidade Estadual De Maringá2021-02-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4564210.4025/actascitechnol.v43i1.45642Acta Scientiarum. Technology; Vol 43 (2021): Publicação contínua; e45642Acta Scientiarum. Technology; v. 43 (2021): Publicação contínua; e456421806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/45642/751375151679Copyright (c) 2021 Acta Scientiarum. Technologyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRossi, Robson MarceloAntunes, Marcos BenattiPelloso, Sandra Marisa2021-03-22T14:20:01Zoai:periodicos.uem.br/ojs:article/45642Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2021-03-22T14:20:01Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Regression models for binary response applied to data on neonatal deaths in newborns |
title |
Regression models for binary response applied to data on neonatal deaths in newborns |
spellingShingle |
Regression models for binary response applied to data on neonatal deaths in newborns Rossi, Robson Marcelo asymmetric link; Bayesian inference; power link. |
title_short |
Regression models for binary response applied to data on neonatal deaths in newborns |
title_full |
Regression models for binary response applied to data on neonatal deaths in newborns |
title_fullStr |
Regression models for binary response applied to data on neonatal deaths in newborns |
title_full_unstemmed |
Regression models for binary response applied to data on neonatal deaths in newborns |
title_sort |
Regression models for binary response applied to data on neonatal deaths in newborns |
author |
Rossi, Robson Marcelo |
author_facet |
Rossi, Robson Marcelo Antunes, Marcos Benatti Pelloso, Sandra Marisa |
author_role |
author |
author2 |
Antunes, Marcos Benatti Pelloso, Sandra Marisa |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Rossi, Robson Marcelo Antunes, Marcos Benatti Pelloso, Sandra Marisa |
dc.subject.por.fl_str_mv |
asymmetric link; Bayesian inference; power link. |
topic |
asymmetric link; Bayesian inference; power link. |
description |
The present study presents binary data modeling regarding 1.6% of neonatal deaths in 3,448 newborns from an epidemiological and observational study with a cross-sectional design, involving the retrospective analysis of 4,293 medical records of high-risk pregnant women followed in a gestational outpatient clinic from September 2012 to September 2017. Different symmetric and asymmetric link functions were considered by means of Bayesian inference. The support of more accurate inferences regarding the parameters of the model will provide biological interpretations that are more reliable and consistent with the reality. The model that presented, significantly, the lowest value for the deviance information criterion (DIC = 398.8), was the binomial with power logit (PL) link function, whose median posterior value estimated and significant for the parameter asymmetry was l = 0.25 (0.14;1.17). This significance is observed in all other models of the power family, however with very different values and significantly higher DIC values, indicating less parsimonious models. The Bayesian methodology proved to be flexible. Additionally, the results show that such model shows an accuracy = 97.4% and area under the ROC curve AUC = 89.4% in the prediction of neonatal deaths based on the weight of children at birth. Specifically, for 2.500g, a value predicted in the medical literature for low weight, the model predicts a probability of 1.43%. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-26 |
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 |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/45642 10.4025/actascitechnol.v43i1.45642 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/45642 |
identifier_str_mv |
10.4025/actascitechnol.v43i1.45642 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/45642/751375151679 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Acta Scientiarum. Technology https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Acta Scientiarum. Technology https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 43 (2021): Publicação contínua; e45642 Acta Scientiarum. Technology; v. 43 (2021): Publicação contínua; e45642 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315336960409600 |