Evaluating the value of checkpoint inhibitor therapy using the ASCO and ESMO frameworks.
Bibliographic record
Abstract
17 Background: The advent of checkpoint inhibitor therapy (CIT) has dramatically changed the oncology landscape, but is associated with significant costs. A positive randomized clinical trial (RCT) may not translate to meaningful outcomes for patients. The American Society of Clinical Oncology (ASCO) and European Society of Medical Oncology (ESMO) have developed frameworks to quantify the value of cancer treatment. We applied these frameworks to RCTs involving CIT in order to explore the relationship between trial outcomes and magnitude of clinical benefit. Methods: A literature search was conducted to identify CIT RCTs. Data extracted included study characteristics, pre-specified estimated hazard ratios (eHR) and observed HR (oHR). ASCO Value Framework version 2016 and ESMO Magnitude of Clinical Benefit (MCB) scale v1.1 were applied to each publication by 2 authors independently. Results: 30 RCTs (3 adjuvant, and 27 advanced setting) using CIT were identified between January 2010- October 2018. The majority of trials were in lung cancer (37%) and melanoma (36%). The eHR was 0.71±0.06 (range: 0.55-0.78), and oHR was 0.76±0.15 (range: 0.49 – 1.11). 54% RCTs did not achieve eHR, with a difference of 0.16±0.12 (range: 0.01 – 0.41). ASCO framework scores ranged -24.0 to 71.3, far below the maximum potential score of 180. 18 RCTs formed the basis for FDA approvals. The mean ASCO framework score was 45.6 ± 16.6 (range: 14.4 - 71.3) for FDA approved indications, and 14.0 ± 18.4 (range: -24 – 49) for non-FDA approvals (p < 0.001). All FDA approvals scored grade 4 or 5 on the ESMO MCB scale, indicating a meaningful clinical benefit. Many non-FDA approved RCTs did not receive an MCB grade as they were negative trials. There was no difference in the ASCO framework scores between MCB grade 4 and 5 RCTs. 3 adjuvant RCTs had an ASCO framework score ranging from 20.5 to 38.7 despite an MCB Grade A. Conclusions: Many trials did not meet the pre-specified eHR. FDA approval had statistically significantly higher NHB scores than non-FDA approvals, and they were deemed to have a meaningful clinical benefit according to the ESMO MCB scale. ASCO framework scores may require re-calibration since the highest score achieved in CIT RCTs was only 40% of the maximum score.
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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 | 0.195 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".