Validity of event-free survival as a surrogate endpoint in haematological malignancy: Review of the literature and health technology assessments
Bibliographic record
Abstract
OBJECTIVE: Event-free survival (EFS) is increasingly used as a primary endpoint in trials of haematological malignancies (HMs). A key consideration is whether EFS can reliably predict survival. METHODS: We conducted a review of the scientific literature and health technology assessments to evaluate evidence for EFS-OS surrogacy in HMs and acceptability of EFS by payers. RESULTS: Evidence of surrogacy varies by indication and line of therapy. In first-line AML, EFS is highly correlated with OS at the trial-level supporting its use as an early endpoint for traditional approval of treatments with curative intent. Surrogacy was also demonstrated in first-line DLBCL but remains unexplored in relapsed/refractory setting where post-transplant EFS24 was not prognostic of survival. In first-line FL, PTCL, T-LBL, and MCL, EFS24 is prognostic of survival but trial-level surrogacy has not yet been evaluated. CONCLUSION: Strong EFS-OS correlation required for surrogacy may only be achievable in HMs with treatments characterised by high rates of durable remissions. Nevertheless, EFS24 is associated with favourable outcomes and remains a clinically meaningful endpoint in HMs.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".