Replicating cardiovascular outcome trials for type 2 diabetes using real-world evidence: protocol for a systematic review of observational studies.
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are the current gold standard for drug safety and efficacy evidence-based regulatory decision making. However, due to their strict inclusion and exclusion criteria trial populations may not be generalizable to the real-world population of interest. (2). RCTS are also resource and time intensive. With the adoption of electronic health records, large amounts of real-world evidence (RWE) on drug exposure and health outcomes are becoming readily available. This is seen as an attractive alternative to evaluate the effectiveness and safety of medical interventions. The US Food and Drug Administration (FDA) and Health Canada are adapting guidelines to incorporate real world evidence in their decision-making. Health Canada has launched an initiative to integrate RWE throughout the life cycle of drugs (3). The FDA is evaulating the potential role of observational studies in contributing to evidence of drug effectiveness (4). They highlight the need to replicate RCTs using rigorously designed observational studies for insight into the opportunities and limitations of using RWE in regulatory decision making(4). In 2008, the FDA issued recommendations that cardiovascular safety trials should be conducted to prove that antidiabetic medications have acceptable cardiovascular risk profiles (5). These recommendations were made in the wake of concerns over increased risk of cardiovascular events from the antidiabetic medication, rosiglitazone, for patients with type 2 diabetes (6). Since then, more than 13 cardiovascular outcome trials have been conducted on antidiabetic drugs for type 2 diabetes including, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide 1 (GLP-1) receptor agonists, and sodium glucose cotransporter-2 (SGLT-2) inhibitors. Broadly speaking, results from these trials demonstrated cardiovascular safety (7). However, these trials were highly selective and may not be generalizable to the larger population. Many of the trials required the presence of high cardiovascular risk factors to increase number of events. Evidence has shown that using the UK’s Royal College of General Practitioners Research and Surveillance Centre database, only 16% of adults with type 2 diabetes had the same high cardiovascular risk factors as those included in the EMPA-REG trial which evaluated SGLT-2 inhibitors (8). For those already initiated on SGLT-2 inhibitors only 11% had a similar risk profile as those included in the trial (8). When the DUPLICATE team emulated cardiovascular outcome trials using US commercial and Medicare patient-level claims data for antidiabetic and antiplatelet medications, they found that only 60% of trials had concordant regulatory conclusions (9). Given that the FDA is striving to understand the complementary nature of RWE to RCTs, coupled with the growing body of evidence on cardiovascular outcome trials in type 2 diabetes and their replication using non-randomized data, we hope to synthesize the information in this area to understand what proportion of RWE patients are eligible for cardiovascular outcome trials, and when restricted to cardiovascular outcome trial eligibility, how patient characteristics and outcomes compare to their respective cardiovascular outcome trials.
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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 | 0.093 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".