Assessing the validity of surrogate endpoints in the context of a controversy about the measurement of effectiveness of hepatitis C virus treatment
Bibliographic record
Abstract
Surrogate endpoints are often used in clinical trials, as they allow for indirect measures of outcomes (eg, shorter trials with less participants). Improvements in surrogate endpoints (eg, reduction in low density lipoprotein cholesterol, normalisation of glycated haemoglobin) achieved with an intervention are, however, not always associated with improvements in patient-important outcomes. The common tendency in evidence-based medicine is to view results based on surrogate endpoints as less certain than results based on long term, final patient-important outcomes and rate them as 'lower quality evidence'. However, careful appraisal of the validity of a surrogate endpoint as a measure of the final, patient-important outcome is more useful than an automatic judgement. In this guide, we use a contemporary and currently highly debated example of the surrogate endpoint 'sustained viral response' (ie, viral eradication considered to represent successful treatment) in patients treated for chronic hepatitis C virus. We demonstrate how the validity of a surrogate endpoint can be critically appraised to assess the quality of the evidence (ie, the certainty in estimates) and the implications for decision-making.
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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 | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.510 | 0.803 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".