The Benefits and Risks of Receiving Investigational Solid Tumor Drugs in Randomized Trials
Bibliographic record
Abstract
BACKGROUND: Many patients participate in cancer trials to access new therapies. The extent to which new treatments produce clinical benefit for trial participants is unclear. PURPOSE: To estimate the progression-free survival (PFS) and overall survival (OS) advantage of assignment to experimental groups in randomized trials for 6 solid tumors. DATA SOURCES: ClinicalTrials.gov was searched for trials of investigational drugs with results posted between 2017 and 2021. STUDY SELECTION: Investigational drugs were defined as those not yet having full approval from the U.S. Food and Drug Administration for the study indication. Trials were included if they were randomized and tested drugs or biologics. DATA EXTRACTION: Data extraction was completed by 2 independent reviewers. Data were pooled using a random-effects model. DATA SYNTHESIS: The sample included 128 trials comprising 141 comparisons of a new drug and a comparator. These comparisons included 47 050 patients. The pooled hazard ratio for PFS was 0.80 (95% CI, 0.75 to 0.85), indicating statistically significant benefit for patients in experimental groups. This corresponded to a median PFS advantage of 1.25 months (CI, 0.80 to 1.68 months). The pooled hazard ratio for OS was 0.92 (CI, 0.88 to 0.95), corresponding to a survival gain of 1.18 months (CI, 0.72 to 1.71 months). The absolute risk for a serious adverse event for comparator group patients was 29.56% (CI, 26.64% to 32.65%), with an increase in risk of 7.40% (CI, 5.66% to 9.14%) for patients in experimental groups. LIMITATIONS: Trials in this sample were heterogeneous. Comparator group interventions were assumed to reflect standard of care. CONCLUSION: Assignment to experimental groups produces statistically significant survival gains. However, the absolute survival gain is small, and toxicity is statistically significantly greater. The findings of this review provide reassuring evidence that patients are not meaningfully disadvantaged by assignment to comparator groups. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.
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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 | 0.548 | 0.791 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".