Comparing Frequentist and Bayesian Quantile Regression Models for Child Hypertension in South Africa
DOI:
https://doi.org/10.6000/Keywords:
Bayesian quantile regression, frequentist quantile regression, paediatric hypertension, South Africa, extreme valuesAbstract
Background:Traditional approaches to modelling paediatric hypertension in South Africa have relied on descriptive or mean regression methods, which inadequately capture risk factors driving the distributional extremes of blood pressure. Quantile regressionprovides a flexible alternative, and Bayesian methods offer advantages in precision and uncertainty estimation, yet their comparative performance has not been assessed in this context.Methods:Nationally representative cross-sectional data from 1,812 adolescents (15–17 years) in the South African National Income Dynamics Study (NIDS) Wave 5 (2017–2018) was analysed. Frequentist and Bayesian quantile regression models were fitted for systolic (SBP) and diastolic blood pressure (DBP) at the 75th and 95th percentiles. Model performance was compared in terms of parameter estimates, interval precision, and convergence diagnostics.Results:BMI and gender were consistent predictors of both SBP and DBP across models. Bayesian quantile regression additionally identified age, race, and pulse rate as significant risk factors for upper quantiles. Bayesian credible intervals were consistently narrower than frequentist confidence intervals, indicating improved precision. Convergence diagnostics confirmed robust posterior inference.Conclusion:Bayesian quantile regression provides more efficient inference than the frequentist alternative when modelling health outcomes concentrated in distributional extremes. This is the first study to apply Bayesian quantileregression to paediatric hypertension in South Africa, demonstrating both methodological value and empirical insights into adolescent health risks
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