Defining Clinically Relevant Target Populations Using Real‐World Data to Guide the Design of Representative Antidiabetic Drug Trials
Bibliographic record
Abstract
The US Food and Drug Administration is considering replacing cardiovascular outcome trials of antidiabetic drugs with trials that better represent patients with type 2 diabetes. However, designing such representative trials requires understanding the underlying target populations (i.e., populations intended to receive the drug in the real-world setting). Thus, we used the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial as a motivating example to illustrate how different target populations impact trial representativeness. Using the United Kingdom Clinical Practice Research Datalink, we identified three target populations: (i) all patients with type 2 diabetes; (ii) patients prescribed liraglutide; and (iii) patients who would have been eligible to receive liraglutide based on treatment stage (i.e., patients with poorly controlled diabetes eligible to receive a second-to-fifth line antidiabetic drug). We then examined the representativeness of the LEADER trial by applying its eligibility criteria to each target population. The target populations of patients with type 2 diabetes (n = 279,763), those prescribed liraglutide (n = 14,421), and those eligible to receive liraglutide based on the treatment stage (n = 85,610) differed substantially in terms of hemoglobin A1c, body mass index, prevalence of heart failure, and chronic kidney disease. Applying the LEADER trial eligibility criteria to these target populations resulted in the inclusion of 19.1%, 20.7%, and 34.8% patients, respectively. This study highlights how real-world data can be used to define different target populations. Explicitly defining these target populations can help in the design of future trials of antidiabetic drugs.
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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 | 0.523 | 0.569 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".