Abstract
Drug development is a continuum. Information from completed trials and other sources may be available when we design a new trial. In this chapter we consider the Bayesian approach to incorporate existing information into future trial planning. Under the Bayesian approach, prior distributions are used to describe the accumulated knowledge about unknown parameters such as those representing treatment effects. The prior distributions are then used to calculate the probability of a successful trial or assurance probability. In this chapter, we look at closed-form expressions for assurance probabilities under some definitions of success. We can use simulations to estimate these probabilities when close-form expressions do not exist. With the use of prior distributions for the parameters of interest, we show how to assess the positive and negative predictive values of a design with its companion decision rule.
Data! Data! Data! I can’t make bricks without clay.
Sir Arthur Conan Doyle
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Chuang-Stein, C., Kirby, S. (2017). Incorporating Information from Completed Trials in Future Trial Planning. In: Quantitative Decisions in Drug Development. Springer Series in Pharmaceutical Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-46076-5_5
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DOI: https://doi.org/10.1007/978-3-319-46076-5_5
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