Emulating randomized controlled trials of long-acting insulins and cardiovascular events using real-world data for patients with type 2 diabetes
Bibliographic record
Abstract
ABSTRACT Aims Randomized controlled trials (RCTs) have high internal validity but often have limited generalizability. To our knowledge, there are no studies examining potential differences between RCTs and real-world data in patient characteristics and risk of major adverse cardiovascular events (MACE) among patients with type 2 diabetes mellitus (T2DM) treated with long-acting insulin analogues. Methods We emulated the DEVOTE trial of insulin degludec vs glargine among patients with T2DM using data from the United Kingdom’s Clinical Practice Research Datalink. DEVOTE eligible and ineligible subpopulations were created. Cox proportional hazards models with inverse probability of treatment weighting were used to estimate hazard ratios (HRs) and corresponding confidence intervals (CIs) for MACE comparing new users of insulin degludec to new users of insulin glargine overall and in the eligible/ineligible subpopulations. Results There were 10,430 patients in the overall population, 5,280 in the DEVOTE eligible population, and 5,150 in the DEVOTE ineligible population. The overall (HR: 1.36, 95% CI: 0.83, 1.86) and DEVOTE eligible populations (HR: 1.07, 95% CI: 0.63, 1.58) were compatible with findings from the DEVOTE trial (HR: 0.91, 95% CI: 0.78, 1.06) for the risk of MACE. Due to a low number of events the DEVOTE ineligible population had deviations in point estimates and wider CIs (HR: 2.19, 95% CI: 0.30, 3.83). Conclusion The risk of MACE among patients with T2DM newly prescribed insulin degludec compared to insulin glargine was consistent between the overall population and the DEVOTE eligible subpopulation, while the DEVOTE ineligible population had discrepant point estimates. Twitter Summary Our study emulated the DEVOTE trial using RWD. Half of the RWD population would be eligible for the trial. RWD and RCTs had compatible effect estimate for risk of major cardiovascular 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 | 0.498 | 0.624 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".