The increasing costs of clinical trials negatively impact public health by reducing drug companies' willingness to undertake clinical trials and delaying new drug development. A typical clinical trial may cost millions of U.S. dollars, depending on therapeutic areas and scientific goals. In designing clinical trials, one needs to balance (typically conflicting) aims in scientific/biological aspects, statistical power, and cost. This decision-making problem is very complicated in personalized medicine, where multiple subpopulations need to be simultaneously considered. Existing approaches for designing adaptive trials either do not involve optimization of objectives or optimize in very restrictive settings. This project considers adaptive enrichment design, a flexible trial design framework that allows trial administrators to adjust patient enrollment rules during the trials. It has been shown to often provide superior cost effectiveness and better statistical power. The research aims to design new methods and algorithms to optimize adaptive enrichment design.
The optimal design problem with two planning stages and two subpopulations is formulated as a large-scale linear programming model, which can be solved by off-the-shelf LP solvers. Due to the exponentially increasing LP size, such LP solvers cannot be directly applied to the practical situations with more planning stages and subpopulations. This project will develop specialized algorithms and modelling techniques to fully exploit problem structures to solve two-stage two-subpopulation models much faster, and extend them to larger models previously regarded as unsolvable. Furthermore, user-friendly open-source software will be developed to enable scientists to construct their own optimal adaptive enrichment designs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
