Matching study design to prescribing intention: The prevalent new‐user design for studying abuse‐deterrent formulations of opioids
Bibliographic record
Abstract
PURPOSE: In drug studies, research designs requiring no prior exposure to certain drug classes may restrict important populations. Since abuse-deterrent formulations (ADF) of opioids are routinely prescribed after other opioids, choice of study design, identification of appropriate comparators, and addressing confounding by "indication" are important considerations in ADF post-marketing studies. METHODS: In a retrospective cohort study using claims data (2006-2018) from a North Carolina private insurer [NC claims] and Merative MarketScan [MarketScan], we identified patients (18-64 years old) initiating ADF or non-ADF extended-release/long-acting (ER/LA) opioids. We compared patient characteristics and described opioid treatment history between treatment groups, classifying patients as traditional (no opioid claims during prior six-month washout period) or prevalent new users. RESULTS: We identified 8415 (NC claims) and 147 978 (MarketScan) ADF, and 10 114 (NC claims) and 232 028 (MarketScan) non-ADF ER/LA opioid initiators. Most had prior opioid exposure (ranging 64%-74%), and key clinical differences included higher prevalence of recent acute or chronic pain and surgery among patients initiating ADFs compared to non-ADF ER/LA initiators. Concurrent immediate-release opioid prescriptions at initiation were more common in prevalent new users than traditional new users. CONCLUSIONS: Careful consideration of the study design, comparator choice, and confounding by "indication" is crucial when examining ADF opioid use-related 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 | 0.121 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), 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".