Abstract
Testing anti-cancer agents with multiple disease subtypes is challenging and it becomes more complicated when the subgroups have different types of endpoints (such as binary endpoints of tumor response and progression-free survival endpoints). When this occurs, one common approach in oncology is to conduct a series of small screening trials in specific patient subgroups, and these trials are typically run in parallel, independent of each other. However, this approach does not consider the possibility that some of the patient subpopulations respond similarly to therapy. In this article, we developed a simple approach to jointly model subgroups with mixed-type endpoints, which allows borrowing strength across subgroups for efficient estimation of treatment effects.
Accepted Version
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
