STatistical Methods for OPtimal Basket Trial Designs fOR Precision Medicine – a General, Customizable TOolbox (STOP OR GO)

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The demand for new treatments in precision medicine remains constantly high. New findings suggest incorporating knowledge on molecular profiling by targeting aberrant genes or pathways right from the beginning of drug development. Therefore, the development of new study designs for early clinical phases is of urgent need. A relatively new approach are so-called “Basket” designs. Basket trials are based on the idea of combining several substudies according to molecular profiling. The individual substudies thereby share a common design. The primary endpoint is usually binary. Other design options might vary, for example the number of study arms per substudy. Basket trials are designed to incorporate interim modification so they include adaptive design elements. By this, they have the chance to increase the efficiency of drug development and to allow developing individualized treatment approaches. However, Basket designs also come along with major statistical challenges including a more difficult sample size planning, multiple testing, interim analyses, the choice of optimal futility boundaries, definition of adequate go/no go criteria and rules for incorporating information exchange (“borrowing”) between different substudies.The global objective of this proposal is to develop optimal Basket designs, which incorporate isolated or combined adaptive elements such that • Clustering/ borrowing, • Futility stopping at interim, • Sample size recalculation. To do so, the proposal divides into three work packages:1. Within the first work package, we aim to improve and extend by reformulating published Basket designs as optimization problems with constraints, which shall be solved analytically. This approach allows 1) to find the optimal parameter solution for a given design and 2) to compare different Basket designs against each other.2. Within the second work package, we aim to develop new adaptive design elements, which are justified by thorough methodologic considerations such that specific performance requirements are met. 3. In a final work package, we will combine the developed constrained optimization problems with the newly adaptive design elements. Subsequently, we will implement a corresponding validated R package with graphical user interface on R CRAN, which allows to design an optimal customizable Basket trial by combining arbitrary adaptive design elements. The results of this project will help to establish efficient novel Basket trials designs in precision medicine. Application of the new designs in practice is supported by the developed validated, freely-available and easy to use software solution.