The design of an oncology clinical program is much more challenging than the design of a separate study. The standard approach has been proven to be not very successful during the last 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 biostatisticians designing and supporting oncology clinical programs. Modeling and simulation approaches can help to optimize an individual trial, to assess the relative impact on other trials in a program, to see the benefit of novel designs, and to increase a probability of success of a clinical program by making better decisions. This presentation is built on the work of the DIA ADSWG oncology sub-tem on an Adaptive Program. With representatives from a number of institutions, this group compared several hypothetical oncology development programs using probability of the clinical program success and expected net present value (eNPV). Simulated scenarios are used to motivate and illustrate the key ideas.