PET-CR as a potential surrogate endpoint in untreated DLBCL: meta-analysis and implications for clinical trial design
Bibliographic record
Abstract
This study's focus is the association of end-of-therapy (EOT) PET results with progression-free (PFS) and overall survival (OS) in patients with diffuse large B-cell lymphoma receiving first-line chemoimmunotherapy. We develop a Bayesian hierarchical model for predicting PFS and OS from EOT PET-complete response (PET-CR) using a literature-based meta-analysis of 20 treatment arms and a substudy of 4 treatment arms in 3 clinical trials for which we have patient-level data. The PET-CR rate in our substudy was 72%. The modeled estimates for hazard ratio (PET-CR/non-PET-CR) were 0.13 for PFS (95% CI 0.10, 0.16) and 0.10 for OS (CI 0.07, 0.12). Hazard ratios varied little by patient subtype and were confirmed by the overall meta-analysis. We link these findings to designing future clinical trials and show how our model can be used in adapting the sample size of a trial to accumulating results regarding treatment benefits on PET-CR and a survival endpoint.
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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 | 0.045 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".