Identification of Potential Surrogate Endpoints in Randomized Clinical Trials of Aggressive Non-Hodgkin Lymphoma: Correlation of Complete Response, Time-to-Event and Overall Survival Data.
Bibliographic record
Abstract
Abstract Abstract 3699 Poster Board III-635 Background Aggressive histology non-Hodgkin lymphomas (NHLs) are generally treated with curative intent. Establishing appropriate surrogate endpoints for overall survival (OS) may permit more rapid evaluation and approval of new agents for aggressive NHL. Treatment failure endpoints including event-free survival (EFS) or progression-free survival (PFS) permit earlier reporting of results, but their ability to predict OS is unknown. The purpose of this study is to correlate different efficacy endpoints with the goal of identifying an appropriate surrogate endpoint for OS. Methods Randomized controlled trials (RCTs) of previously untreated aggressive histology NHL published between 1990-2009 were identified through a systematic literature search using MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials databases. Eligible RCTs included at least 2-arms comparing different systemic treatments with ≥100 patients/arm. Studies investigating the effect of autologous stem-cell transplant and those exclusively involving T-cell lymphoma, mantle cell lymphoma or HIV-associated lymphoma were excluded. Baseline characteristics, design, treatment arms, efficacy endpoints, and results were extracted from each published RCT. Reported survival endpoints were defined as PFS, EFS, or OS according to established (ie: per protocol) definitions in the International Working Group Revised Response Criteria for Lymphoma. Absolute differences in efficacy endpoints were determined and nonparametric Spearman rank correlation coefficients were calculated to determine the association between differences in: 1) CR and each of EFS, PFS and OS and 2) EFS or PFS and OS. Results Thirty-eight RCTs were identified representing 85 treatment arms and 16,103 patients. The median follow up was 55 months (range 20-108). The most commonly used primary endpoint was OS (55%) followed by EFS (32%), but use of CR as a primary endpoint was infrequent (5%). Differences in CR strongly correlated with differences in 3-yr EFS with a Spearman rank correlation coefficient of 0.88 (95% CI: 0.57 to 0.97). The Spearman rank correlation coefficients between differences in CR and differences in 3-yr PFS and 5-yr OS were 0.62 (95% CI: 0.21 to 0.84) and 0.50 (95% CI, 0.23 to 0.74), respectively. Differences in intermediate endpoints, 3-yr PFS or EFS, were high correlated with differences in 5 yr OS with a Spearman rank correlation coefficient of 0.90 (95%CI, 0.73-0.96). Similarly strong correlations were noted when 3-yr PFS and 3-yr EFS were each correlated with 5-yr OS separately. Linear regression determined that a 10% improvement in CR is estimated to correspond with a 9±1% improvement in 3-yr EFS and that a 10% improvement in 3-yr EFS or PFS would predict for a 7±1% improvement in 5-yr OS. Conclusions In RCTs of initial treatment for aggressive NHL, improvements in 3-yr EFS/PFS are highly correlated with improvements in 5-yr OS. Changes in CR rates are a strong predictor for changes in 3-yr EFS, but not for changes in 5-yr OS. This may inform future trial design since EFS or PFS appear to be appropriate surrogate endpoints for OS in this patient population. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.326 | 0.541 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".