Evolution of Randomized Trials in Advanced/Metastatic Soft Tissue Sarcoma: End Point Selection, Surrogacy, and Quality of Reporting
Bibliographic record
Abstract
PURPOSE: Randomized controlled trials (RCTs) in soft tissue sarcoma (STS) have used varying end points. The surrogacy of intermediate end points, such as progression-free survival (PFS), response rate (RR), and 3-month and 6-month PFS (3moPFS and 6moPFS) with overall survival (OS), remains unknown. The quality of efficacy and toxicity reporting in these studies is also uncertain. METHODS: A systematic review of systemic therapy RCTs in STS was performed. Surrogacy between intermediate end points and OS was explored using weighted linear regression for the hazard ratio for OS with the hazard ratio for PFS or the odds ratio for RR, 3moPFS, and 6moPFS. The quality of reporting for efficacy and toxicity was also evaluated. RESULTS: Fifty-two RCTs published between 1974 and 2014, comprising 9,762 patients, met the inclusion criteria. There were significant correlations between PFS and OS (R = 0.61) and between RR and OS (R = 0.51). Conversely, there were nonsignificant correlations between 3moPFS and 6moPFS with OS. A reduction in the use of RR as the primary end point was observed over time, favoring time-based events (P for trend = .02). In 14% of RCTs, the primary end point was not met, but the study was reported as being positive. Toxicity was comprehensively reported in 47% of RCTs, whereas 14% inadequately reported toxicity. CONCLUSION: In advanced STS, PFS and RR seem to be appropriate surrogates for OS. There is poor correlation between OS and both 3moPFS and 6moPFS. As such, caution is urged with the use of these as primary end points in randomized STS trials. The quality of toxicity reporting and interpretation of results is suboptimal.
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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 | 0.795 | 0.914 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".