End Points for Adjuvant Therapy Trials: Has the Time Come to Accept Disease-Free Survival as a Surrogate End Point for Overall Survival?
Bibliographic record
Abstract
The intent of adjuvant therapy is to eradicate micro-metastatic residual disease following curative resection with the goal of preventing or delaying recurrence. The time-honored standard for demonstrating efficacy of new adjuvant therapies is an improvement in overall survival (OS). This typically requires phase III trials of large sample size with lengthy follow-up. With the intent of reducing the cost and time of completing such trials, there is considerable interest in developing alternative or surrogate end points. A surrogate end point may be employed as a substitute to directly assess the effects of an intervention on an already accepted clinical end point such as mortality. When used judiciously, surrogate end points can accelerate the evaluation of new therapies, resulting in the more timely dissemination of effective therapies to patients. The current review provides a perspective on the suitability and validity of disease-free survival (DFS) as an alternative end point for OS. Criteria for establishing surrogacy and the advantages and limitations associated with the use of DFS as a primary end point in adjuvant clinical trials and as the basis for approval of new adjuvant therapies are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.419 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".