mAED: Multi-Stage Adaptive Enrichment Design in R

The average cost of trials in the United States is up to $19.6 million for a Phase 2 trial and $52.9 million for a Phase 3 trial. Optimizing the clinical trial design is essential to decrease the cost of drug development. This becomes more complicated in the setup of personalized medicine and multi-stage adaptive enrichment design (mAED), where there are multiple subpopulations and multiple stages for decision making. The current approaches formulate mAED problem as a general linear programming (LP) problem, which is computationally expensive to solve. In this project, we aim to develop new customized algorithms and R package for this problem with three key features: 1) It provides a highly efficient solver to tackle a large and important class of LP problems; 2) It provides a solution for multi-stage decision-making problems with Bayes risk constraints; 3) It provides additional functions such as visualizing the optimal decision maps.