Failures in Phase III: Causes and Consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phase III randomized controlled trials (RCT) in oncology fail to lead to registration of new therapies more often than RCTs in other medical disciplines. Most RCTs are sponsored by the pharmaceutical industry, which reflects industry's increasing responsibility in cancer drug development. Many preclinical models are unreliable for evaluation of new anticancer agents, and stronger evidence of biologic effect should be required before a new agent enters the clinical development pathway. Whenever possible, early-phase clinical trials should include pharmacodynamic studies to demonstrate that new agents inhibit their molecular targets and demonstrate substantial antitumor activity at tolerated doses in an enriched population of patients. Here, we review recent RCTs and found that these conditions were not met for most of the targeted anticancer agents, which failed in recent RCTs. Many recent phase III RCTs were initiated without sufficient evidence of activity from early-phase clinical trials. Because patients treated within such trials can be harmed, they should not be undertaken. The bar should also be raised when making decisions to proceed from phase II to III and from phase III to marketing approval. Many approved agents showed only better progression-free survival than standard treatment in phase III trials and were not shown to improve survival or its quality. Introduction of value-based pricing of new anticancer agents would dissuade the continued development of agents with borderline activity in early-phase clinical trials. When collaborating with industry, oncologists should be more critical and better advocates for cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.330 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it