The use of surrogate endpoints in clinical trials: focus on clinical trials in cardiovascular diseases
Bibliographic record
Abstract
Surrogate endpoints include a wide range of laboratory or physical measurements used in clinical trials as a substitute for meaningful clinical endpoints that directly assess effects of the intervention(s) tested on mortality and/or morbidity. These surrogate endpoints are frequently employed in clinical trials and when used judiciously, can accelerate and focus the study of new therapies and can greatly enhance our understanding of their mechanisms of action. The current review provides a definition of surrogate endpoints, proposes practical criteria for establishing their validity, outlines some of the advantages, disadvantages and specific statistical considerations associated with their use in clinical trials and attempts also to highlight drug approval issues associated with the use of these endpoints. A number of examples are also provided related to the use of surrogate endpoints in clinical trials with special emphasis on their use in cardiovascular medicine.
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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 | 0.097 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| 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; 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".