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.
Keywords: Bayesian; cancer subgroups; early phase oncology trial; hierarchical model; mixed endpoints.