Comparative analysis of the phase III clinical trials of anti-PD1 monotherapy in head and neck squamous cell carcinoma patients (CheckMate 141 and KEYNOTE 040)
Bibliographic record
Abstract
Two phase III clinical trials (CheckMate 141 and KEYNOTE 040) have independently demonstrated that overall survival (OS) in recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC) patients, who have failed platinum-based therapy, can be improved with anti-PD1 monotherapy. Treatment with nivolumab or pembrolizumab in R/M HNSCC patients led to an improved OS with a hazards ratio (HR) of 0.70 (95%CI 0.51-0.96; p = 0.01) and HR of 0.80 (95%CI 0.65-0.98, p = 0.0161), respectively, as compared to standard of care (SOC) chemo monotherapy regimens (specifically, cetuximab, docetaxel, or methotrexate). The gain in OS was similar in both studies, underscoring the role of anti-PD1 drugs in R/M HNSCC patients. One of the striking discrepancies between CheckMate 141 and KEYNOTE 040 was the OS observed in the control SOC arms (6.9 months median in KEYNOTE 040 versus 5.1 months in CheckMate 141), which inadvertently set a higher threshold in the bio-statistical analysis of KEYNOTE 040 so that the clinical outcome of every patient was influential in the analysis. We perform a comparative analysis of the two studies to identify potential factors in the control arm that can impact clinical trial bio-statistical outcomes and which may have implications for future immunotherapy clinical trial designs.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".