Redesigning a Stopped Clinical Trial as an Emulated Trial Using Real-World Data to Explore the Effectiveness of Slow-Release Oral Morphine as a Treatment for Opioid Use Disorder
Bibliographic record
Abstract
Canada is currently experiencing a problematic opioid crisis with increasing mortality rates. Traditional randomized controlled trials (RCTs) that examine the effectiveness of pharmacological treatment options for people with opioid use disorder (OUD) are challenging to conduct. An increasingly popular methodology is through the implementation of emulated clinical trials, a methodology in which key elements of a "target" RCT are replicated using previously collected healthcare-based data. They can possibly address some of the common challenges found in the conduct of RCTs, such as prolonged timelines, high cost, and poor participant recruitment. In effect, emulated trials accelerate knowledge generation by producing real-world evidence that can be akin to phase 3 effectiveness trials, without any need to recruit live participants or administer investigational products. During the COVID-19 pandemic, several trials were stopped due to increased pandemic-related research restrictions, leaving important questions about OUD treatment unanswered. In this commentary, we describe the transition of a traditional RCT to an emulated trial spurred by challenges posed by the COVID-19 pandemic. We describe our transition using a notable published framework with regards to the population sample, interventions, outcomes, and proposed analyses. This commentary aims to help other researchers and trialists apply emulated trials in substance use research and beyond, emphasizing the role of this methodology in clinical research and advancing scientific knowledge that could be otherwise lost or unattainable.
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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 | 0.441 | 0.706 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.024 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".