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  • Quantitative Decisions in Drug Development

    Quantitative Decisions in Drug Development by Chuang-Stein, Christy; Kirby, Simon;

    Sorozatcím: Springer Series in Pharmaceutical Statistics;

      • 12% KEDVEZMÉNY?

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        45 203 Ft (43 051 Ft + 5% áfa)
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    45 203 Ft

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    A termék adatai:

    • Kiadás sorszáma 1st ed. 2017
    • Kiadó Springer, Berlin
    • Megjelenés dátuma 2017. január 1.
    • Kötetek száma 1 pieces, Book

    • ISBN 9783319460758
    • Kötéstípus Keménykötés
    • Terjedelem248 oldal
    • Méret 239x162x21 mm
    • Súly 552 g
    • Nyelv angol
    • Illusztrációk 16 SW-Abb., 11 Farbabb. Illustrations, black & white
    • 0

    Kategóriák

    Rövid leírás:

    This book offers a high-level treatise of evidence-based decisions in drug development. Because of the inseparable relationship between designs and decisions, a good portion of this book is devoted to the design of clinical trials. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. The latter include selecting appropriate metrics to formulate decisions criteria, determining go/no-go decisions for progressing a drug candidate to the next stage and predicting the effectiveness of a product. Lastly, it points out common mistakes made by drug developers under the current drug-development paradigm.

    The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization.

    The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.

    Több

    Hosszú leírás:

    This book offers a high-level treatise of evidence-based decisions in drug development. Because of the inseparable relationship between designs and decisions, a good portion of this book is devoted to the design of clinical trials. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. The latter include selecting appropriate metrics to formulate decisions criteria, determining go/no-go decisions for progressing a drug candidate to the next stage and predicting the effectiveness of a product. Lastly, it points out common mistakes made by drug developers under the current drug-development paradigm.

    The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization.

    The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.



    "This work offers useful algorithms, classifications, and other general points to statisticians or 'quantitative scientists'. But, it is also really useful to regulatory affairs managers, clinicians, medical writers, and all kinds of decision-makers in the industry." (Andrei Myslivets, ISCB News, Vol. 68, December, 2019)
    "It is presented in a concise, structured, friendly, and illustrative way that allows for a good understanding of the underlying ideas ... . the book from Chuang-Stein and Kirby is a valuable, interesting and easy read for statisticians and clinicians with some methodological background who are involved in clinical development or drug approval and who are looking for a structured way to make clinical development decisions." (Norbert Benda, Biometrical Journal, Vol. 61 (4), July, 2016)

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