Titlebook: Quantitative Decisions in Drug Development; Christy Chuang-Stein,Simon Kirby Book 2021Latest edition The Editor(s) (if applicable) and The

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Christy Chuang-Stein,Simon KirbyFocuses on important decision points and evidence needed for making decisions at these points during the development of a new drug.Takes a holistic approach towards drug development by incorporating k

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Quantitative Decisions in Drug Development978-3-030-79731-7Series ISSN 2366-8695 Series E-ISSN 2366-8709

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https://doi.org/10.1007/978-3-030-79731-7clinical trials; metric for decision; drug development; probability of study success; probability of pro

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Book 2021Latest editions a holistic approach towards drug development by incorporating explicitly knowledge learned from the earlier part of the development and available historical information into decisions at later stages. In addition, the book shares lessons learned from several select examples published in the litera

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Incorporating Information from Completed Trials and Other Sources in Future Trial Planning,ssurance probabilities under some definitions of success. We can use simulations to estimate these probabilities when closed-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.

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,Designing Dose–Response Studies with Desired Characteristics,nse study design, for both continuous and binary endpoints. We include an example of dose–response studies for an investigational medicinal product to illustrate the importance of covering an adequate dose range in dose-ranging studies.

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Discounting Prior Results to Account for Selection Bias,e power of the subsequent Phase 3 trial if a Phase 3 trial is to be conducted. While we have hinted at the existence of selection bias in earlier chapters, Chapter 12 is the first time we consider this issue thoroughly. We offer some recommendations on how to watch out for selection bias when planning for late-stage trials.