Identification of potential surrogate end points in randomized clinical trials of aggressive and indolent non-Hodgkin's lymphoma: correlation of complete response, time-to-event and overall survival end points
Bibliographic record
Abstract
BACKGROUND: The correlation between efficacy end points in randomized controlled trials (RCTs) of systemic therapy for non-Hodgkin's lymphoma (NHL) was investigated to identify an appropriate surrogate end point for overall survival (OS). METHODS: RCTs of previously untreated NHL published from 1990 to 2009 were identified. Associations between absolute differences in efficacy end points were determined using nonparametric Spearman's rank correlation coefficients (r(s)). RESULTS: Thirty-eight RCTs representing 85 treatment arms for aggressive NHL and 20 RCTs representing 42 arms for indolent NHL were included. For aggressive NHL, differences in 3-year progression-free survival (PFS)/event-free survival (EFS) were high correlated with differences in 5-year OS {r(s) of 0.90 [95% confidence interval (CI) 0.73-0.96]} and linear regression determined that a 10% improvement in 3-year EFS or PFS would predict for a 7% ± 1% improvement in 5-year OS. For indolent histology disease, differences in complete response were strongly correlated with differences in 3-year EFS [r(s) 0.86 (95% CI 0.35-0.97)], but there was no correlation between 3-year time-to-event end points and 5-year OS. CONCLUSIONS: Improvements in 3-year EFS/PFS are highly correlated with improvements in 5-year OS in aggressive NHL and should be explored as a candidate surrogate end point. Definition of these relationships may inform future clinical trial design and interpretation of interim trial data.
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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 | 0.196 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| 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".