Measuring the long-term “tail of curve” survival benefits in oncology trials: A comparison of the ASCO Value Framework and the ESMO Magnitude of Clinical Benefit Scale.
Bibliographic record
Abstract
2585 Background: Recently, anti-cancer agents have generated excitement due to their capacity to preserve long-term survival in some patients, represented by a “tail of the survival curve”. However, as traditional measures of clinical benefit may not accurately capture long-term survival, amendments to various valuation frameworks have been proposed to capture this benefit. The purpose of this study was to determine how frequently immune checkpoint inhibitor vs. non-immune checkpoint inhibitor anti-cancer agents, displayed trends of long-term survival, as defined by the American Society of Clinical Oncology Value Framework (ASCO-VF) and European Society of Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS), as well as to analyze the degree of agreement between ASCO and ESMO frameworks. Methods: Anti-cancer agents from phase II or III randomized controlled trials (RCTs) cited for clinical efficacy evidence in drug approval by the Food and Drug Administration (FDA) between January 2011 and March 2018 were identified. Data required for ASCO-VF and ESMO-MCBS were extracted. Difference in how often long-term survival bonuses were awarded were calculated in all RCTs, as well as immune checkpoint inhibitor and non-immune checkpoint inhibitor RCTs individually. Cohen’s Kappa statistic was calculated to evaluate agreement between ASCO-VF and ESMO-MCBS. Results: 100 RCTs were analyzed. RCTs were awarded ASCO-VF version 2 (v2) “tail of the curve” bonuses more often than ESMO-MCBS version 1.1 (v1.1) “immunotherapy-triggered” long-term plateau adjustments (45% vs. 2.6%). Comparing to non-immune checkpoint inhibitor RCTs, immune checkpoint inhibitor RCTs were not more likely to receive ASCO-VF v2 bonuses/ESMO-MCBS v1.1 adjustments (p = 0.32/ p = 0.40). Long-term survival agreement between the two frameworks was poor (kappa: 0.01; p = 0.50). Conclusions: The ASCO-VF v2 and ESMO-MCBS v1.1 may require additional refinement in order to accurately capture the benefit of long-term survival or immune checkpoint inhibitor and non-immune checkpoint inhibitor agents may not preserve substantially different long-term survival populations.
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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 | 0.387 | 0.651 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".