Designing Dose-Optimization Studies in Cancer Drug Development: Discussions with Regulators
Bibliographic record
Abstract
The article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums on March 18th, June 10th, and July 8th of 2021, organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the U.S. Food and Drug Administration (FDA) Oncology Center of Excellence and the LUNGevity Foundation. Diverse stakeholders including oncologists, patient advocates, experts from regulatory agencies across the world, academicians, and representatives from the pharmaceutical industry engaged in a lively discussion on strategies for and designs of dose-optimization studies in cancer drug development. Dose-optimization is one of the major challenges in oncology drug development. The discussions were focused on considerations in designing dose-optimization studies of products for treatment of cancer patients in pre-approval and post-approval stages. Presenters and panelists discussed diverse ideas and methods and agreed that a shift in paradigm is required in oncology drug development that should improve dose optimization while not unnecessarily delaying patient access to potentially efficacious new treatments.
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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 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.019 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".