Association between control group therapy and magnitude of clinical benefit of cancer drugs.
Bibliographic record
Abstract
e18604 Background: Oncology societies have developed tools to quantify the magnitude of clinical benefit. These include the American Society of Clinical Oncology Value Framework (ASCO-VF) version 2, the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) version 1.1, the National Comprehensive Cancer Network (NCCN) Evidence Blocks and the ASCO Cancer Research Committee criteria (ASCO-CRC). Associations between the characteristics of approved drugs and magnitude of clinical benefit have been explored in depth. Here, we assess the association between characteristics of control group therapy and magnitude of clinical benefit. Methods: We searched Drugs@FDA to identify new solid tumor cancer drugs approved based on randomized trials (RCTs) between January 2012 and December 2021. Substantial clinical benefit was defined as: overall survival gain ≥ 2.5 months and progression-free survival gain ≥ 3 months for the ASCO-CRC (palliative setting only); ASCO-VF threshold score ≥45 (palliative and curative setting); NCCN Evidence Blocks threshold score ≥16 (palliative and curative setting); and grade A or B for trials of curative intent and 4 or 5 for those of non-curative intent using ESMO-MCBS. Associations between characteristics of control group therapy (e.g. type of active therapy, use of matched placebo and overlap between experimental and control therapy) and substantial clinical benefit scores were explored using logistic regression. Results: We identified 174 RCTs supporting the approval of 76 drugs for 164 indications. Of these, 47% (82/174) were placebo-controlled trials among which 42% (34/82) comprised active treatment with a matched placebo. Substantial clinical benefit was observed in 45%, 46%, 72% and 73% using the ESMO-MCBS, ASCO-VF, NCCN Evidence Blocks and ASCO-CRC, respectively. These low proportions resulted in an inability to fit multivariable models adequately. RCTs with a control group comprising of active treatment with a matched placebo were associated with significantly lower odds of substantial benefit with ESMO-MCBS (OR 0.27, P = .003) and ASCO-VF (OR 0.30, P = .008) but not with NCCN Evidence Blocks (OR 0.74, P = .55) or ASCO-CRC criteria (OR 1.36, P = .54). Similar results were observed when excluding trials in the curative setting. There was a non-significant association with higher odds of substantial benefit with ESMO-MCBS with trials in which the control group was chemotherapy (OR 1.97, P = .07). For ASCO-CRC a non-significant association in the opposite direction was observed (OR 0.40, P = .06). Conclusions: Clinical benefit scales can be sensitive to the type of control group therapy. RCTs with an active treatment and matched placebo in the control group were less likely to be scored as providing substantial clinical benefit using the ESMO-MCBS and the ASCO-VF scales. Control group therapy did not influence NCCN Evidence Blocks or ASCO-CRC scores.
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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 | 0.053 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".