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

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 (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Alhamzawi, R., Yu, K., & Benoit, D. F. (2012). Bayesian adaptive lasso quantile regression. Statistical Modelling, 12 (3), 279–297.
2. Benoit, D. F., & Van den Poel, D. (2009). Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services. Expert Systems with Applications, 36 (7), 10475–10484.
3. Datta, J., & Ghosh, J. K. (2013). Asymptotic properties of bayes risk for the horseshoe prior.
4. Gómez, G., Calle, M. L., & Oller, R. (2004). Frequentist and bayesian approaches for intervalcensored data. Statistical Papers, 45, 139–173.
5. Hainmueller, J., & Hazlett, C. (2014). Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. Political Analysis, 22 (2), 143–168.
6. He, F., Zhou, J., Feng, Z.-k., Liu, G., & Yang, Y. (2019). A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with bayesian optimization algorithm. Applied energy, 237, 103–116.
7. Hothorn, T., & Everitt, B. S. (2014). A handbook of statistical analyses using r. CRC press.
8. Kim, J. S., Shah, A. A., Hummers, L. K., & Zeger, S. L. (2021). Predicting clinical events using bayesian multivariate linear mixed models with application to scleroderma. BMC medical research methodology, 21, 1–12.
9. Klau, S., Jurinovic, V., Hornung, R., Herold, T., & Boulesteix, A.-L. (2018). Priority-lasso: A simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC bioinformatics, 19 (1), 1–14.
10. Koenker, (2004). Quantile Regression.
11. Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33–50.
12. Koenker, R., & Mizera, I. (2014). Convex optimization, shape constraints, compound decisions, and empirical bayes rules. Journal of the American Statistical Association, 109 (506), 674–685.
13. Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for bayesian quantile regression.
14. Leung, A. A., Daskalopoulou, S. S., Dasgupta, K., McBrien, K., Butalia, S., Zarnke, K. B., Nerenberg, K., Harris, K. C., Nakhla, M., Cloutier, L., et al. (2017). Hypertension canada’s 2017 guidelines for diagnosis, risk assessment, prevention, and treatment of hypertension in adults. Canadian Journal of Cardiology, 33 (5), 557–576.
15. Liu, X., Saat, M. R., Qin, X., & Barkan, C. P. (2013). Analysis of us freight-train derailment severity using zero-truncated negative binomial regression and quantile regression. Accident Analysis & Prevention, 59, 87–93.1565–1578.
16. Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society Series B: Statistical Methodology, 72 (4), 417–473.
17. Sala-i-Martin, X., Doppelhofer, G., & Miller, R. I. (2004). Determinants of long-term growth: A bayesian averaging of classical estimates (bace) approach. American economic review, 94 (4), 813–835.