Comparators in Pharmacovigilance: A Quasi-Quantification Bias Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: It is unclear which comparator is the most appropriate for bias reduction in disproportionality analyses based on spontaneous reports. We conducted a quasi-quantitative bias analysis using two well-studied drug-event combinations to assess how different comparators influence the directionality of bias in pharmacovigilance. METHODS: We used the US Food and Drug Administration Adverse Event Reporting System focusing on two drug-event combinations with a propensity for stimulated reporting: rivaroxaban and hepatotoxicity, and canagliflozin and acute kidney injury. We assessed the directionality of three disproportionality analysis estimates (reporting odds ratio, proportional reporting ratio, information component) using one unrestricted comparator (full data) and two restricted comparators (active comparator, active comparator with class exclusion). Analyses were conducted within two calendar time periods, defined based on external events (approval of direct oral anticoagulants, Food and Drug Administration safety warning on acute kidney injury with sodium-glucose cotransporter 2 inhibitors) hypothesized to alter reporting rates. RESULTS: There were no false-positive signals for rivaroxaban and hepatotoxicity irrespective of the comparator. Restricting to the initial post-approval period led to false-positive signals, with restricted comparators performing worse. There were false-positive signals for canagliflozin and acute kidney injury, with restricted comparators performing better. Restricting to the period before the Food and Drug Administration warning weakened the false-positive signal for canagliflozin and acute kidney injury across comparators. CONCLUSIONS: We could not identify a consistent and predictable pattern to the directionality of disproportionality analysis estimates with specific comparators. Calendar time-based restrictions anchored on relevant external events had a considerable impact.
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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 | 0.472 | 0.602 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.024 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".