Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models

Kirui Dennis; Charity Wamwea; Bonface Malenje; Levi Bor

doi:doi:10.11648/j.ajtas.20231206.13

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Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models

Kirui Dennis^*, Charity Wamwea, Bonface Malenje, Levi Bor

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)

Received: 3 October 2023 Accepted: 25 October 2023 Published: 11 November 2023

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)
DOI	10.11648/j.ajtas.20231206.13
Page(s)	174-179
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

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

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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. https://doi.org/10.11648/j.ajtas.20231206.13

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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

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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

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@article{10.11648/j.ajtas.20231206.13,
  author = {Kirui Dennis and Charity Wamwea and Bonface Malenje and Levi Bor},
  title = {Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {6},
  pages = {174-179},
  doi = {10.11648/j.ajtas.20231206.13},
  url = {https://doi.org/10.11648/j.ajtas.20231206.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.13},
  abstract = {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.
},
 year = {2023}
}

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TY  - JOUR
T1  - Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models
AU  - Kirui Dennis
AU  - Charity Wamwea
AU  - Bonface Malenje
AU  - Levi Bor
Y1  - 2023/11/11
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.13
DO  - 10.11648/j.ajtas.20231206.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 174
EP  - 179
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231206.13
AB  - 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.

VL  - 12
IS  - 6
ER  -

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Author Information

Kirui Dennis

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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Charity Wamwea

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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Bonface Malenje

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Levi Bor

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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Plain Text BibTeX RIS

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. https://doi.org/10.11648/j.ajtas.20231206.13

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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

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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

Copy | Download

@article{10.11648/j.ajtas.20231206.13,
  author = {Kirui Dennis and Charity Wamwea and Bonface Malenje and Levi Bor},
  title = {Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {6},
  pages = {174-179},
  doi = {10.11648/j.ajtas.20231206.13},
  url = {https://doi.org/10.11648/j.ajtas.20231206.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.13},
  abstract = {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.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models
AU  - Kirui Dennis
AU  - Charity Wamwea
AU  - Bonface Malenje
AU  - Levi Bor
Y1  - 2023/11/11
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.13
DO  - 10.11648/j.ajtas.20231206.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 174
EP  - 179
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231206.13
AB  - 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.

VL  - 12
IS  - 6
ER  -

Copy | Download