Considerations on the use of different comparators in pharmacovigilance: A methodological review
Bibliographic record
Abstract
Pharmacovigilance studies based on spontaneous reporting systems use disproportionality analysis methods to identify drug-event combinations with higher-than-expected reporting. Enhanced reporting is deemed as a proxy for a detected signal and is used to generate drug safety hypotheses, which can then be tested in pharmacoepidemiologic studies or randomized controlled trials. Higher-than-expected reporting means that the reporting rate of a drug-event combination of interest is disproportionately higher than the rate in a specific comparator or reference set. Currently, it is unclear which comparator is the most appropriate for use in pharmacovigilance. Moreover, it is also unclear how the selection of a comparator may affect the directionality of the various reporting and other biases. This paper reviews commonly used comparators chosen for signal detection studies (active comparator, class-exclusion comparator, and full data reference set). We give an overview of the advantages and disadvantages of each method based on examples from the literature. We also touch upon the challenges related to the derivation of general recommendations for the selection of comparators when mining spontaneous reports for pharmacovigilance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.282 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".