Asset Details
Do you wish to reserve the book?
Bayesian adaptive design for targeted therapy development in lung cancer — a step toward personalized medicine
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Bayesian adaptive design for targeted therapy development in lung cancer — a step toward personalized medicine
Do you wish to request the book?
Bayesian adaptive design for targeted therapy development in lung cancer — a step toward personalized medicine
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Bayesian adaptive design for targeted therapy development in lung cancer — a step toward personalized medicine
2008
Overview
Background: With the advancement in biomedicine, many biologically targeted therapies have been developed. These targeted agents, however, may not work for everyone. Biomarker profiles can be used to identify effective targeted therapies. Our goals are to characterize the molecular signature of individual tumors, offer the best-fit targeted therapies to patients in a study, and identify promising agents for future development.
Methods: We propose an outcome-based adaptive randomization trial design for patients with advanced stage non-small cell lung cancer. All patients have baseline biopsy samples taken for biomarker assessment prior to randomization to treatments. The primary endpoint of this study is the disease control rate at 8 weeks after randomization. The Bayesian probit model is used to characterize the disease control rate. Patients are adaptively randomized to one of four treatments with the randomization rate based on the updated disease control rate from the accumulated data in the trial. For each biomarker profile, high-performing treatments have higher randomization rates, and vice versa. An early stopping rule is implemented to suspend low-performing treatments from randomization.
Results: Based on extensive simulation studies, with a total of 200 evaluable patients, our trial has desirable operating characteristics to: (1) identify effective agents with a high probability; (2) suspend ineffective agents; and (3) treat more patients with effective agents that correspond to their biomarker profiles. Our trial design continues to update and refine the estimates as the trial progresses.
Limitations: This biomarker-based trial requires biopsible tumors and a two-week turn around time for biomarker profiling before randomization. Additionally, in order to learn from the interim data and adjust the randomization rate accordingly, the outcome-based adaptive randomization design is applicable only for trials when the endpoint can be assessed in a relative short period of time.
Conclusion: Bayesian adaptive randomization trial design is a smart, novel, and ethical design. In conjunction with an early stopping rule, it can be used to efficiently identify effective agents, eliminate ineffective ones, and match effective treatments with patients' biomarker profiles. The proposed design is suitable for the development of targeted therapies and provides a rational design for personalized medicine. Clinical Trials 2008; 5: 181—193. http://ctj.sagepub.com
Publisher
SAGE Publications,Sage Publications Ltd
