Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation
DOI:
https://doi.org/10.6000/1929-6029.2024.13.02Keywords:
Wide dataset, Logistic regression, Markov chain Monte Carlo, Covariates, Bayesian computation, Bayes information criterion.Abstract
Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.
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