Optimism Bias in the Design of Phase III Randomized Control Trials Evaluating PD-1/PD-L1 Targeting Monoclonal Antibodies
Bibliographic record
Abstract
BACKGROUND: Many randomized control trials (RCTs) evaluating programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) targeting monoclonal antibodies (mAbs) have been completed or are in progress. We examined hypothesized hazard ratios (HHRs) and observed hazard ratios (OHRs) from published RCTs evaluating these mAbs. METHODS: Publications of RCTs evaluating at least one PD-1/PD-L1 targeting mAbs approved by the US Food and Drug Administration were identified through PubMed searches. The primary reports of RCTs were retrieved. Two investigators extracted HHR, OHR for the primary endpoint among other data elements independently. The differences (∆HR) in HHR and OHR were analyzed statistically. A separate search was conducted for secondary reports after longer follow-ups, the updated OHR was extracted. RESULTS: Forty-nine RCTs enrolling 36 867 patients were included. The mean HHR and OHR were 0.672 and 0.738 respectively. The mean ∆HR was 0.067 (range: -0.300 to 0.895; 95% confidence interval (CI), 0.003-0.130). HHR was met or exceeded in 22 (45%) RCTs. OHR was ≥ 1.0 in 6 RCTs (12%). PD-L1 expression was not associated with the magnitude of effect. Of 18 RCTs with follow-up reports, the magnitude of benefit decreased in 8 RCTs with extended follow-ups. CONCLUSION: The majority of published RCTs evaluating PD-1/PD-L1 targeting mAbs did not achieve their hypothesized magnitude of benefit. The optimism bias requires attention from the cancer clinical research community given the number of these agents in development and the intense interest in evaluating these agents in a variety of disease settings.
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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 | 0.731 | 0.795 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".