Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact in evaluating the risk factors that are responsible for mortality and morbidity during IHD (Ischemic heart...

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Detalles Bibliográficos
Autores: Ghosh, Sarada, Samanta, Guruprasad, De la Sen Parte, Manuel
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/52650
Acceso en línea:http://hdl.handle.net/10810/52650
Access Level:acceso abierto
Palabra clave:zero inflated model
Bayesian inference
Gibbs sampling
Markov Chain Monte Carlo
log-likelihood
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spelling Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart DiseaseGhosh, SaradaSamanta, GuruprasadDe la Sen Parte, Manuelzero inflated modelBayesian inferenceGibbs samplingMarkov Chain Monte Carlolog-likelihoodIschemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact in evaluating the risk factors that are responsible for mortality and morbidity during IHD (Ischemic heart disease). In general, geometric or Poisson distributions can underestimate the zero-count probability and hence make it difficult to identify significant effects of covariates for improving conditions of heart disease due to regional wall motion abnormalities. In this work, a flexible class of zero inflated models is introduced. A Bayesian estimation method is developed as an alternative to traditionally used maximum likelihood-based methods to analyze such data. Simulation studies show that the proposed method has a better small sample performance than the classical method, with tighter interval estimates and better coverage probabilities. Although the prevention of CAD has long been a focus of public health policy, clinical medicine, and biomedical scientific investigation, the prevalence of CAD remains high despite current strategies for prevention and treatment. Various comprehensive searches have been performed in the MEDLINE, HealthSTAR, and Global Health databases for providing insights into the effects of traditional and emerging risk factors of CAD. A real-life data set is illustrated for the proposed method using WinBUGS.This research was funded by the Spanish Government for its support through grant RTI2018-094336-B-100 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19.MDPI2021202120212021info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/52650reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MCIU/RTI2018-094336-B-100/https://www.mdpi.com/2227-9717/9/7/1242/htminfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/2021 by the authors. 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/).oai:addi.ehu.eus:10810/526502026-06-18T09:23:17Z
dc.title.none.fl_str_mv Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
title Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
spellingShingle Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
Ghosh, Sarada
zero inflated model
Bayesian inference
Gibbs sampling
Markov Chain Monte Carlo
log-likelihood
title_short Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
title_full Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
title_fullStr Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
title_full_unstemmed Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
title_sort Bayesian Analysis for Cardiovascular Risk Factors in Ischemic Heart Disease
dc.creator.none.fl_str_mv Ghosh, Sarada
Samanta, Guruprasad
De la Sen Parte, Manuel
author Ghosh, Sarada
author_facet Ghosh, Sarada
Samanta, Guruprasad
De la Sen Parte, Manuel
author_role author
author2 Samanta, Guruprasad
De la Sen Parte, Manuel
author2_role author
author
dc.subject.none.fl_str_mv zero inflated model
Bayesian inference
Gibbs sampling
Markov Chain Monte Carlo
log-likelihood
topic zero inflated model
Bayesian inference
Gibbs sampling
Markov Chain Monte Carlo
log-likelihood
description Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact in evaluating the risk factors that are responsible for mortality and morbidity during IHD (Ischemic heart disease). In general, geometric or Poisson distributions can underestimate the zero-count probability and hence make it difficult to identify significant effects of covariates for improving conditions of heart disease due to regional wall motion abnormalities. In this work, a flexible class of zero inflated models is introduced. A Bayesian estimation method is developed as an alternative to traditionally used maximum likelihood-based methods to analyze such data. Simulation studies show that the proposed method has a better small sample performance than the classical method, with tighter interval estimates and better coverage probabilities. Although the prevention of CAD has long been a focus of public health policy, clinical medicine, and biomedical scientific investigation, the prevalence of CAD remains high despite current strategies for prevention and treatment. Various comprehensive searches have been performed in the MEDLINE, HealthSTAR, and Global Health databases for providing insights into the effects of traditional and emerging risk factors of CAD. A real-life data set is illustrated for the proposed method using WinBUGS.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/52650
url http://hdl.handle.net/10810/52650
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MCIU/RTI2018-094336-B-100/
https://www.mdpi.com/2227-9717/9/7/1242/htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
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