Postmyocardial Infarction Statin Exposure and the Risk of Stroke with Weighting for Outcome Misclassification
Bibliographic record
Abstract
BACKGROUND: Observational healthcare data can be used for drug safety and effectiveness research. The use of inverse probability of treatment weights (IPW) reduces measured confounding under the assumption of accurate measurement of the outcome variable; however, many datasets suffer from systematic outcome misclassification. METHODS: We introduced a modification to IPW to correct for the presence of outcome misclassification. To demonstrate the utility of these modified weights in realistic settings, we investigated postmyocardial infarction statin use and the 1-year risk of stroke in the Clinical Practice Research Datalink. RESULTS: We computed an IPW-adjusted odds ratio (OR = 0.67; 95% confidence interval (CI) = 0.48, 0.93). We employed a technique to modify IPW for the presence of outcome misclassification using linked hospital records for outcome validation (modified IPW adjusted OR = 0.77; 95% CI = 0.52, 1.15) and compared the results with a meta-analysis of randomized controlled trials (RCTs) (pooled OR = 0.80; 95% CI = 0.74, 0.87). Finally, we present simulation studies to investigate the impact of model selection on bias reduction and variability. CONCLUSION: Ignoring outcome misclassification yielded biased estimates whereas the use of the modified IPW approach produced encouraging results when compared with the meta-analytic RCT findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".