Application of Value Frameworks to the Design of Clinical Trials: The Canadian Cancer Trials Group Experience
Bibliographic record
Abstract
BACKGROUND: Use of value framework thresholds in the design of clinical trials may increase the proportion of randomized controlled trials that identify clinically meaningful advances for patients. Existing frameworks have not been applied to the research output of a cooperative cancer trials group. We apply value frameworks to the randomized controlled trial output of the Canadian Cancer Trials Group (CCTG). METHODS: Statistical design, study characteristics, and results of all published phase III trials of CCTG were abstracted. We applied the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) and American Society of Clinical Oncology Net Health Benefit to study results and the statistical power calculations to identify the proportion of all trials that were designed to detect a substantial clinical benefit. RESULTS: During 1979 to 2017, CCTG published 113 phase III trials; 52.2% (59 of 113) of these trials were positive. One-half (50.4%, 57 of 113) of the trials were conducted in the palliative setting. In 37.2% (42 of 113) of trials, the primary endpoint was overall survival; disease-free survival or progression-free survival was used in 38.9% (44 of 113) of trials. The ESMO-MCBS could be applied to the power calculation for 69 trials; 73.9% (51 of 69) of these trials were designed to detect an effect size that could meet ESMO-MCBS thresholds for substantial benefit. Among the 51 positive trials for which the ESMO-MCBS could be applied, 41.1% (21 of 51) met thresholds for substantial benefit. CONCLUSIONS: Most CCTG phase III trials were designed to detect clinically meaningful differences in outcome, although less than one-half of positive trials met the threshold for substantial benefit. Application of value frameworks to the design of clinical trials is practical and may improve research efficiency and treatment options for 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.
How this classification was reachedexpand
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 | 0.071 | 0.552 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".