Understanding the role of hypertension in stroke outcomes using Bayesian analysis
Bibliographic record
Abstract
A comorbid hypertension was previously associated with survival advantages in patients with stroke. We aimed to explore how strong priors for the hypertension covariate affect the reverse association, as a way to test the sensitivity of reverse epidemiology findings to bias assumptions. The authors used stroke data from 2014 to 2019 (N = 177,947) and subsequently performed random sampling from a population of various sizes. The data were analyzed using Bayesian multiple logistic mixed-effects regression, which was further modelled in three scenarios: with informative (strong) priors, non-informative priors, and accounting for the interaction mechanism (age*hypertension). In addition, we perform a series of sensitivity analyses to check the robustness of the estimates to different prior choices. Both informative and non-informative priors demonstrated elevated posterior odds ratios (ORs) for hypertension in low sample fractions (n = 100-500). As the sample size increased, the ORs declined (below 1) for each subsequently larger samples. The ORs plateaued as the sample exceeded 5000 and became similar for both the modeling scenarios. Conversely, the interaction term revealed inverse patterns, increasing in effect as the sample size grew large. Thus, the reverse effect of hypertension diminishes with age. Although further modifications of prior precision revealed somewhat higher ORs for hypertension covariate, the estimates mostly overlapped. Bayesian analysis may improve the interpretation of reverse associations when data are limited; however, in large datasets, their influence diminishes. This pattern suggests that reverse associations reflect collider or selection bias rather than prior choice and that Bayesian priors alone cannot address design bias.
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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 | 0.048 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".