Cover Image

Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia

The majority of research that looked at the Bayesian Markov Chain Monte Carlo (MCMC) approach for prognostic modelling of cardiovascular disease only focused on the use of the Bayesian approach in variable selection, model selection, and prior distribution selection. But very few of this research ha...

Full description

Saved in:
Bibliographic Details
Main Authors: Nurliyana Juhan, Yong Zulina Zubairi, Ahmad Syadi Mahmood Zuhdi, Zarina Mohd Khalid
Format: Article
Language:English
Published: Penerbit Akademia Baru 2024
Subjects:
RA407-409.5 Health status indicators. Medical statistics and surveys
RC666-701 Diseases of the circulatory (Cardiovascular) system
Online Access:https://eprints.ums.edu.my/id/eprint/42773/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42773/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ums.eprints.42773
record_format eprints
spelling my.ums.eprints.427732025-02-06T05:50:31Z https://eprints.ums.edu.my/id/eprint/42773/ Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia Nurliyana Juhan Yong Zulina Zubairi Ahmad Syadi Mahmood Zuhdi Zarina Mohd Khalid RA407-409.5 Health status indicators. Medical statistics and surveys RC666-701 Diseases of the circulatory (Cardiovascular) system The majority of research that looked at the Bayesian Markov Chain Monte Carlo (MCMC) approach for prognostic modelling of cardiovascular disease only focused on the use of the Bayesian approach in variable selection, model selection, and prior distribution selection. But very few of this research has looked at the Markov chains' convergence in the model. In this study, convergence diagnostics were carried out to evaluate the convergence of Markov chains using both visual inspection and additional diagnostics. The National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry, which included 1248 female patients with ST-Elevation Myocardial Infarction (STEMI) between 2006 and 2013, was used for this study's analysis. The multivariate Bayesian model identified six significant variables: dyslipidaemia, myocardial infarction, smoking, renal disease, Killip class, and age group. The trace plots did not reveal any distinctive patterns based on these significant variables, and the model's MCMC mixing is typically good. While for the autocorrelation plots, mild autocorrelations for age group, Killip IV, as well as the intercept term in the model. Since there were only mild autocorrelations, no thinning is needed. Also, the Geweke diagnostic showed that the chain is divided into two windows containing a set fraction of the first and last iterations which produced standard Z-scores. The Geweke diagnostic did not provide evidence of non-convergence, as none of the Z-scores fell in the extreme tails of the N (0,1). In this study, a number of plots and additional diagnostic tools showed that the Markov chains have reached convergence, which is relevant to the general use of the MCMC approach. Penerbit Akademia Baru 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42773/1/FULL%20TEXT.pdf Nurliyana Juhan and Yong Zulina Zubairi and Ahmad Syadi Mahmood Zuhdi and Zarina Mohd Khalid (2024) Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology. pp. 1-11. ISSN 2462-1943
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic RA407-409.5 Health status indicators. Medical statistics and surveys
RC666-701 Diseases of the circulatory (Cardiovascular) system
spellingShingle RA407-409.5 Health status indicators. Medical statistics and surveys
RC666-701 Diseases of the circulatory (Cardiovascular) system
Nurliyana Juhan
Yong Zulina Zubairi
Ahmad Syadi Mahmood Zuhdi
Zarina Mohd Khalid
Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
description The majority of research that looked at the Bayesian Markov Chain Monte Carlo (MCMC) approach for prognostic modelling of cardiovascular disease only focused on the use of the Bayesian approach in variable selection, model selection, and prior distribution selection. But very few of this research has looked at the Markov chains' convergence in the model. In this study, convergence diagnostics were carried out to evaluate the convergence of Markov chains using both visual inspection and additional diagnostics. The National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry, which included 1248 female patients with ST-Elevation Myocardial Infarction (STEMI) between 2006 and 2013, was used for this study's analysis. The multivariate Bayesian model identified six significant variables: dyslipidaemia, myocardial infarction, smoking, renal disease, Killip class, and age group. The trace plots did not reveal any distinctive patterns based on these significant variables, and the model's MCMC mixing is typically good. While for the autocorrelation plots, mild autocorrelations for age group, Killip IV, as well as the intercept term in the model. Since there were only mild autocorrelations, no thinning is needed. Also, the Geweke diagnostic showed that the chain is divided into two windows containing a set fraction of the first and last iterations which produced standard Z-scores. The Geweke diagnostic did not provide evidence of non-convergence, as none of the Z-scores fell in the extreme tails of the N (0,1). In this study, a number of plots and additional diagnostic tools showed that the Markov chains have reached convergence, which is relevant to the general use of the MCMC approach.
format Article
author Nurliyana Juhan
Yong Zulina Zubairi
Ahmad Syadi Mahmood Zuhdi
Zarina Mohd Khalid
author_facet Nurliyana Juhan
Yong Zulina Zubairi
Ahmad Syadi Mahmood Zuhdi
Zarina Mohd Khalid
author_sort Nurliyana Juhan
title Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
title_short Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
title_full Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
title_fullStr Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
title_full_unstemmed Evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in Malaysia
title_sort evaluating mcmc convergence in a bayesian model of st-elevation myocardial infarction female patients in malaysia
publisher Penerbit Akademia Baru
publishDate 2024
url https://eprints.ums.edu.my/id/eprint/42773/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42773/
_version_ 1823533990992150528
score 13.244413

Similar Items