Definitions, acceptability, limitations, and guidance in the use and reporting of surrogate end points in trials: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To synthesize the current literature on the use of surrogate end points, including definitions, acceptability, and limitations of surrogate end points and guidance for their design/reporting, into trial reporting items. STUDY DESIGN AND SETTING: Literature was identified through searching bibliographic databases (until March 1, 2022) and gray literature sources (until May 27, 2022). Data were thematically analyzed into four categories: (1) definitions, (2) acceptability, (3) limitations and challenges, and (4) guidance, and synthesized into reporting guidance items. RESULTS: After screening, 90 documents were included: 79% (n = 71) had data on definitions, 77% (n = 69) on acceptability, 72% (n = 65) on limitations and challenges, and 61% (n = 55) on guidance. Data were synthesized into 17 potential trial reporting items: explicit statements on the use of surrogate end point(s) and justification for their use (items 1-6); methodological considerations, including whether sample size calculations were informed by surrogate validity (items 7-9); reporting of results for composite outcomes containing a surrogate end point (item 10); discussion and interpretation of findings (items 11-14); plans for confirmatory studies, collecting data on the surrogate end point and target outcome, and data sharing (items 15-16); and informing trial participants about using surrogate end points (item 17). CONCLUSION: The review identified and synthesized items on the use of surrogate end points in trials; these will inform the development of the Standard Protocol Items: Recommendations for Interventional Trials-SURROGATE and Consolidated Standards of Reporting Trials-SURROGATE extensions.
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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 | 0.687 | 0.848 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.037 | 0.034 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".