Bayesian Optimal Phase II Design with Futility and Efficacy Stopping Boundaries
BOP2-FE design for binary endpoint
BOP2-FE design for co-primary endpoint
BOP2-FE design for joint efficacy and toxicity endpoint
BOP2-FE design for nested (ordinal) endpoint
Boundary values for binary Endpoint
Boundary values for co-primary Endpoint
Boundary values for joint Endpoint
Boundary values for Nested Endpoint
Computes both the boundary and corresponding operating characteristics...
Computes both the boundary and corresponding operating characteristics...
Computes both the boundary and corresponding operating characteristics...
Computes both the boundary and corresponding operating characteristics...
Compute Probability Cutoffs for Futility and efficacy Stopping
Operating characteristics for binary Endpoint
Operating characteristics for for coprimary Endpoint
Operating characteristics for for joint Endpoint
Operating characteristics for Nested Endpoint
Plot the cut-off probability and simulation results for BOP2FE designs
Search optimal parameters for binary endpoint
Search optimal parameters for co primary endpoint
Search optimal parameters for joint efficacy and toxicity endpoint
Search optimal parameters for nested endpoint
Compute operating characteristics at the optimal boundary
summarize main results for a given BOP2FE designs
Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the design’s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) <doi:10.1080/10543406.2025.2558142>.