Multistage phase II design for mixed tumor response and time-to-event endpoints: An application for screening new drugs in hepatocellular carcinoma (HCC).
Bibliographic record
Abstract
e14706 Background: Phase II trials aim to assess the anti-tumor activity of investigational therapies, and consider if they warrant further study. In some instances such as a study treatment added to standard therapy in HCC, tumor response alone may not provide a clear picture on its effectiveness (e.g. biologics and targeted therarpy). Other endpoints such as progression-free survival (PFS) in addition to conventional tumor response would increase the chance of detecting useful treatment and also be able to terminate a study earlier if the treatment was deemed ineffective. Methods: Following a similar rationale for multinomial endpoints in phase II trials by Zee (1999), we have developed a multi-stage phase II stopping rule for "mixed tumor response and time-to-event endpoints". We used a study entitled, “Randomized Phase II study of the x-linked inhibitor of apoptosis (XIAP) antisense AEG35156 in combination with sorafenib in patients with advanced HCC” as an illustration. We applied this multi-stage stopping rule for mixed endpoints in a randomized phase II setting, where the control arm was being used here as a way to set up the null hypothesis. We defined the null hypothesis by a mixture of response rate of 5% and PFS of 2.6 months versus an alternative hypothesis of response rate of 20% and PFS of 5.2 months. Results: The stopping rule was such that the null hypothesis would be rejected at a correlation of 0.5 for the mixed endpoints and conclude that the treatment is effective if we have 0-1 responders and a PFS>=4.0 months, or 2 responders and PFS>=3.8 months, or 3 responders and a PFS>=3.6 months, or 4 responders and a PFS>=3.0 months, or 5 responders with any PFS. Conclusions: In the AEG35156 study, we had 3 responders (based on Choi’s criteria), and 1 responder even if we used RECIST criteria. A PFS of 4.0 months. Therefore, we concluded that the study treatment in combination with sorafenib has a positive effect and warrants further investigation. This methodology would greatly improve the efficiency for phase II screening especially on new biologics on top of a standard or for diseases where tumor response alone does not reflect the full effectiveness of the new treatment.
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 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 | 0.124 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".