Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations

The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs. However, evaluating trial design alternatives is difficult; the designs may have different advantages-better power, better type I error control, shorter duration, or more accuracy-and their relative performance may depend on assumptions about the drugs' performance. Evaluating different early phase designs in particular suffers from both these problems. This paper is built on the work of the DIA's Adaptive Design Scientific Working Group oncology subteam on an Adaptive Program. With representatives from a number of institutions, this group compared 4 hypothetical oncology development programs that each was to select between 2 treatments and decide whether to proceed to phase 3, using probability of the clinical program's success and expected net present value (eNPV). Simulated scenarios were used to motivate and illustrate the key ideas. For the development strategies, we believed that the eNPV showed a distinct and robust improvement for each successive strategy.