Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE
| Autor: | George Poste, Patrick Y. Wen, W. K. Alfred Yung, Webster K. Cavenee, Susan M. Chang, Donald A. Berry, Brian M. Alexander, Robert Mittman, Mitchel S. Berger, Sujuan Ba, Timothy F. Cloughesy, Wenbin Li, Mustafa Khasraw, Tao Jiang, Anna D. Barker |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Research design
Cancer Research medicine.medical_specialty Population Disease Bioinformatics 03 medical and health sciences 0302 clinical medicine Clinical Trials Phase II as Topic medicine Biomarkers Tumor Humans Medical physics Stage (cooking) education Randomized Controlled Trials as Topic education.field_of_study business.industry Brain Neoplasms Learning environment Bayes Theorem Survival Analysis Clinical trial Oncology Clinical Trials Phase III as Topic Research Design 030220 oncology & carcinogenesis Biomarker (medicine) business Glioblastoma 030217 neurology & neurosurgery Agile software development |
| Zdroj: | Clinical cancer research : an official journal of the American Association for Cancer Research. 24(4) |
| ISSN: | 1557-3265 |
| Popis: | Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737–43. ©2017 AACR. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|