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Abstract
Modern drug development requires an efficient clinical development program to have a reasonable chance of successfully leading to the submission of the therapy, given that the therapy is effective, or to early stopping if this is not the case. Clinical trials and programs should be designed to effectively support this final goal. Currently, the statistical planning in drug development is based on parts of a clinical program in isolation, conditioned on one fixed setting, focusing on sample size calculation or simple design questions. There is, however, an increasing demand for a clinical program optimization and acceleration as well as an unconditional evaluation of relative program efficiency, robustness, and validity. The complexity of the development process, however, often does not allow for simple solutions, frequently requiring computer simulations to support these assessments.
We propose a general framework for comparing competing options for clinical programs, trial designs, and analysis methods as a basis for decision making and to facilitate the internal and external dialogue with key stakeholders. The final decision making ultimately needs to factor in quantitative aspects as well as additional qualitative dimensions such as logistic feasibility, regulatory acceptance, and so on. A terminology is introduce that clearly describes the different aspects of such a framework, the range of underlying assumptions, the competing options, and the metrics that are used to assess and compare these options.
Three specific case studies are presented that illustrate these concepts at three different levels: program planning, trial design, and analysis methods.
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