Comparing Frequentist and Bayesian Quantile Regression Models for Child Hypertension in South Africa
Bibliographic record
Abstract
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 regression provides 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 quantile regression to paediatric hypertension in South Africa, demonstrating both methodological value and empirical insights into adolescent health risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".