Time‐to‐event surrogate end‐points in multiple myeloma randomised trials from 2005 to 2019: A surrogacy analysis
Bibliographic record
Abstract
Use of surrogate end-points such as progression-free survival (PFS) and other time-to-event (TTE) end-points is common in multiple myeloma (MM) clinical trials. This systematic review characterises all published randomised controlled trials (RCTs) in MM using PFS or other TTE end-points between 2005 and 2019 and assesses strength of surrogacy of PFS for overall survival (OS). The association between OS hazard ratios (HRs) and PFS HRs was evaluated with linear regression, and the coefficient of determination with Pearson's correlation. We identified 88 RCTs of which 67 (76%) used PFS as the primary/co-primary end-point. One trial indicated whether progression was biochemical or clinical. Of the variance in OS, 39% was due to variance in PFS. Correlation between PFS and OS was weak (0.62, 95% confidence interval [CI] 0.38-0.78). In newly diagnosed MM, 43% of the variance in OS was due to changes in PFS. The correlation between PFS and OS was weak (0.65, 95% CI 0.30-0.84). In relapsed/refractory MM, 58% of the variance in OS was due to changes in PFS. Correlation between PFS and OS was medium (0.76, 95% CI 0.42-0.91). We demonstrate that PFS and progression characteristics are characterised poorly in MM trials and that PFS is a poor surrogate for OS in MM.
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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 | 0.141 | 0.404 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".