Allopurinol and Cardiovascular Events: <scp>Time‐Related</scp> Biases in Observational Studies
Bibliographic record
Abstract
Objective Several observational studies reported that allopurinol, an effective treatment for gout, was associated with important reductions in cardiovascular (CV) events, with calls for large, randomized trials, although some results were conflicting. The present study was undertaken to assess the extent of time‐related biases in these observational studies. Methods We searched the literature for all observational studies reporting on allopurinol and CV events, focusing on 2 time‐related biases. Time‐related confounding bias results from studies using cohorts of patients all exposed to allopurinol, with comparisons based on episodes of allopurinol discontinuation, where confounding factors are not updated over follow‐up time. Immortal time bias arises from the exposure misclassification of periods of cohort follow‐up during which the outcome under study cannot occur. Results We identified 12 studies, of which 8 were affected by time‐related confounding bias or immortal time bias, while the remaining 4 studies avoided these biases. The studies affected by time‐related confounding bias resulted in significant reductions in the incidence of CV events with allopurinol use (pooled hazard ratio [HR] 0.88 [95% confidence interval (95% CI) 0.85–0.92]), as did the studies affected by immortal time bias (pooled HR 0.79 [95% CI 0.72–0.87]). The 4 studies that avoided these biases resulted in a pooled HR of 1.07 (95% CI 0.91–1.25). Conclusion Observational studies reporting significantly reduced incidence of CV events with allopurinol use were affected by time‐related biases. Overall, studies that avoided these biases did not find a protective effect. The ALL‐HEART randomized trial will provide important and accurate evidence on the potential effectiveness of allopurinol on CV outcomes.
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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 | 0.249 | 0.462 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".