Target trial emulation of cardiovascular outcome trials of medications used to treat type 2 diabetes using real-world data: a systematic review of observational studies
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cardiovascular outcome trials are mandated by the US Food and Drug Administration to assess the cardiovascular safety of new antidiabetic medications before entering the market. However, these trials often involve highly selective populations and results may not generalize to routine practice. METHODS: Our study aimed to synthesize observational studies to assess the generalizability of cardiovascular outcome trials to routine practice. We systematically reviewed observational studies that were target trial emulations of previous cardiovascular outcome trials for dipeptidyl peptidase-4 inhibitors, glucagon-like peptide 1 (GLP-1) receptor agonists, and sodium glucose cotransporter-2 (SGLT-2) inhibitors among patients with type 2 diabetes. We searched the MEDLINE, EMBASE, and Cochrane databases for observational studies that focused on trial emulation or cross-sectional studies that reported the proportion of real-world patients eligible for completed trials. RESULTS: Nineteen studies were included in our systematic review, including four cohort studies that were target trial emulations of previous randomized controlled trials (RCTs) and 15 cross-sectional studies that evaluated trial eligibility. Results between RCTs and real-world data (RWD) were concordant for all drug classes in finding noninferiority. The median eligibility percentage ranged from 13% to 31% for SGLT-2 inhibitor trials and 12% to 43% for GLP-1 receptor agonist trials. CONCLUSION: These results suggest that, while RCTs and RWD are concordant in their estimates, the trials lack representativeness. More research is needed on the emulation of cardiovascular outcome trials using RWD to understand how different emulation methods may impact findings.
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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 | Systematic review | low |
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.167 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".