Surrogate outcomes: experiences at the Common Drug Review
Bibliographic record
Abstract
BACKGROUND: Surrogate outcomes are a significant challenge in drug evaluation for health technology assessment (HTA) agencies. The research objectives were to: identify factors associated with surrogate use and acceptability in Canada's Common Drug Review (CDR) recommendations, and compare the CDR with other HTA or regulatory agencies regarding surrogate concerns. METHODS: Final recommendations were identified from CDR inception (September 2003) to December 31, 2010. Recommendations were classified by type of outcome (surrogate, final, other) and acceptability of surrogates (determined by the presence/absence of statements of concern regarding surrogates). Descriptive and statistical analyses examined factors related to surrogate use and acceptability. For thirteen surrogate-based submissions, recommendations from international HTA and regulatory agencies were reviewed for statements about surrogate acceptability. RESULTS: Of 156 final recommendations, 68 (44%) involved surrogates. The overall 'do not list' (DNL) rate was 48%; the DNL rate for surrogates was 41% (p = 0.175). The DNL rate was 64% for non-accepted surrogates (n = 28) versus 25% for accepted surrogates (odds ratio 5.4, p = 0.002). Clinical uncertainty, use of economic evidence over price alone, and a premium price were significantly associated with non-accepted surrogates. Surrogates were used most commonly for HIV, diabetes, rare diseases, cardiovascular disease and cancer. For the subset of drugs studied, other HTA agencies did not express concerns for most recommendations, while regulatory agencies frequently stated surrogate acceptance. CONCLUSIONS: The majority of surrogates were accepted at the CDR. Non-accepted surrogates were significantly associated with clinical uncertainty and a DNL recommendation. There was inconsistency of surrogate acceptability across several international agencies. Stakeholders should consider collaboratively establishing guidelines on the use, validation, and acceptability of surrogates.
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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 | 0.269 | 0.483 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".