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Research Article |

Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models

The assessment of stroke risk and mortality, the second leading global cause of death, is of paramount importance. Stroke prediction is a vital pursuit due to its multifactorial nature, involving variables like age, sex, gender, hypertension, BMI and heart disease, which introduce considerable complexity. These diverse factors often lead to substantial uncertainty in stroke prediction models. Our research delves into the evaluation of two distinct methodologies for quantifying this uncertainty: Bayesian and classical quantiles. Bayesian quantiles are calculated from the posterior distribution of a Bayesian logistic regression model, accounting for prior information and spatial correlations. In contrast, classical quantiles are based on the assumption that stroke probabilities conform to a normal distribution. The results reveal that, across all coefficients, the Bayesian model produces narrower intervals compared to the classical model, indicating higher accuracy and confidence. Hence, we conclude that Bayesian quantiles outperform classical quantiles in the context of stroke prediction in Kenya. We recommend their adoption in future research and applications, acknowledging their superior performance and reliability in enhancing stroke prediction models, ultimately contributing to improved public health outcomes. This research represents a significant step towards a better understanding and management of stroke risks and mortality on a global scale.

Bayesian Quantile Regression, Classical Quantile Regression, Potential Scale Reduction Factor, Markov Chain Monte Carlo

APA Style

Dennis, K., Wamwea, C., Malenje, B., Bor, L. (2023). Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models. American Journal of Theoretical and Applied Statistics, 12(6), 174-179.

ACS Style

Dennis, K.; Wamwea, C.; Malenje, B.; Bor, L. Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models. Am. J. Theor. Appl. Stat. 2023, 12(6), 174-179. doi: 10.11648/j.ajtas.20231206.13

AMA Style

Dennis K, Wamwea C, Malenje B, Bor L. Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models. Am J Theor Appl Stat. 2023;12(6):174-179. doi: 10.11648/j.ajtas.20231206.13

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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