Subgroup identification of targeted therapy effects on biomarker for time to event data
Bibliographic record
Abstract
BACKGROUND: The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. OBJECTIVES: This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. METHODS: A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. RESULTS: The MTA is observed with range 12-16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. CONCLUSION: This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting 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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".