Active‐comparator restricted disproportionality analysis for pharmacovigilance signal detection studies of chronic disease medications: An example using sodium/glucose cotransporter 2 inhibitors
Bibliographic record
Abstract
AIMS: Disproportionality analysis is a common pharmacovigilance tool to detect safety signals of type 2 diabetes medications from spontaneous drug reporting databases. The aim was to demonstrate the impact of using active-comparator restricted disproportionality analysis (ACR-DA), wherein the reference group is restricted to reports with a clinically appropriate active comparator. METHODS: Using reports from the Food and Drug Administration Adverse Event Reporting System, we assessed if sodium/glucose cotransporter 2 (SGLT2) inhibitors are associated with higher reporting of 5 potential adverse events: acute kidney injury, genitourinary tract infections, diabetic ketoacidosis, fractures, and amputations. For each adverse event, we calculated the proportional reporting ratio (PRR) and adjusted reporting odds ratio (aROR [95% confidence interval, CI]) using 3 types of reference groups: no SGLT2 inhibitor (background risk reference), other diabetes drugs (therapeutic class reference), and dipeptidyl peptidase 4 inhibitors (active comparator reference). RESULTS: Based on ACR-DA, we did not detect a safety signal for acute kidney injury (PRR 0.92 [0.81-1.04]; aROR 0.78 [95% CI 0.72-0.85]) or fractures (PRR 0.44[95% CI 0.17-1.15]; aROR 0.74 [95% CI 0.61-0.91]) associated with SGLT2 inhibitors compared to dipeptidyl peptidase 4 inhibitors. However, we detected safety signals for genitourinary tract infections (PRR 2.75[2.02-3.76]; aROR 2.54[2.26-2.86], diabetic ketoacidosis (PRR 63.85[39.37-103.53; aROR 91.49[70.66-118.48]), and amputations (PRR 52.60 [19.66-140.75]; aROR 22.64 [15.32-33.42]. CONCLUSION: The use of the proposed ACR-DA to detect safety signals of type 2 diabetes medications may reduce false positive safety signals through careful selection of the comparator which is expected to reduce channelling bias.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".