Surrogate endpoints for HTA decisions of breast cancer drugs: utility and pitfalls
Bibliographic record
Abstract
PURPOSE OF REVIEW: Health technology assessment (HTA) of cancer drugs is important to identify whether drugs should be publicly funded. With increasing use of surrogate end points in clinical trials including breast cancer, a review of literature was done to synthesize evidence for validation of these surrogate end points and their potential role in HTA decisions pertaining to breast cancer. FINDINGS: Disease free survival (DFS) in human epidermal receptor 2 (HER2) positive early breast cancer remains the only validated surrogate end point. Other surrogate end points like pathological complete response (pCR) and event free survival (EFS) in early breast cancer (EBC) and objective response rate (ORR) and progression free survival (PFS) in advanced disease have not been validated for overall survival (OS). Moreover, surrogate end points for quality of life (QOL) have not been established and drugs that improve PFS can have detrimental effect on QOL. End points like pCR have excellent prognostic utility in individual patients but have weak correlation with survival at trial level. SUMMARY: Most surrogate end points used in breast cancer do not predict OS or QOL which makes it challenging to use them for decisions regarding public funding of cancer drugs. These findings are relevant to HTA agencies prior to making drug reimbursement decisions.
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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 | 0.079 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".