When Are "Positive" Clinical Trials in Oncology Truly Positive?
Bibliographic record
Abstract
The approval of a new drug for cancer treatment by the regulatory authorities, such as the United States Food and Drug Administration or European Medicines Agency, is usually based on the positive results of one or more randomized phase III clinical trials comparing the investigational treatment with the standard treatment. A clinical trial is presented as positive if the new drug tested on an experimental group shows a statistically significant difference with the control group (P < .05) in the primary endpoint, which is usually a time-to-event endpoint (overall survival or progression-free survival). Such apparently positive clinical trials disregard whether the final value of the difference in the primary endpoints between the experimental and control groups (δ) meets the criterion that was predefined in the protocol. Currently, the trend is to design large trials that may detect statistically significant, but often trivial, differences in survival endpoints. However, recent appeals have been made in the oncology literature for the design of smaller clinical trials to detect or exclude only larger, clinically important, values of δ. Here, we have evaluated 18 randomized phase III clinical trials that were used for the approval of molecular-targeted anticancer drugs by the United States Food and Drug Administration. Results showed that in some of the articles the magnitude of the reported values of δ were lower than the values predefined in the protocol. We suggest that trials should not be declared positive based only on a statistically significant P value, but should also require detection of a difference in survival outcome that equals or exceeds a clinically important value that is specified in the protocol.
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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 | 0.727 | 0.853 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.027 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".