American Journal of Theoretical and Applied Statistics

Research Article | | Peer-Reviewed |

Modelling Stroke Risk Factors Using Classical and Bayesian Quantile Regression Models

Received: Oct. 03, 2023    Accepted: Oct. 25, 2023    Published: Nov. 11, 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.

DOI 10.11648/j.ajtas.20231206.13
Published in American Journal of Theoretical and Applied Statistics ( Volume 12, Issue 6, November 2023 )
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), 2024. 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://download.sciencepg.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
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    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
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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

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

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

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