Model-assisted Bayesian designs for dose finding and optimization : methods and applications First edition

Model-assisted Bayesian designs for dose finding and optimization : methods and applications

First edition

Material type: 図書

Author: Ying Yuan, Ruitao Lin, J. Jack Lee

Publisher: C&Hall/CRC Press

Publication date: 2023

Material Format: Paper

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NDC: -

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Note (General)：: Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)Includes bibliographical references and indexSumm...

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Paper

Material Type: 図書
ISBN: 9780367146245
Title: Model-assisted Bayesian designs for dose finding and optimization : methods and applications
Author/Editor: Ying Yuan, Ruitao Lin, J. Jack Lee
Edition: First edition
Author Heading: Yuan, Ying (Professor of biostatistics)
Lin, Ruitao
Lee, J. Jack
Publication, Distribution, etc.: C&Hall/CRC Press
Publication Date: 2023
Publication Date (W3CDTF): 2023
Place of Publication (Country Code): us
Text Language Code: en
Subject Heading: Drug Development -- methods
Drug Design -- methods
Drug Dosage Calculations
Models, Statistical
Bayes Theorem
Clinical Trials, Phase I as Topic
LCC: RM301.25
Target Audience: 一般
Note (General): Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)
Includes bibliographical references and index
Summary: "Bayesian adaptive designs provide a critical approach to improve the efficiency and success rate of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they forms the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describes the state-of-the-art model-assisted designs to faciliate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol-making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the
Related Material (URI): https://ci.nii.ac.jp/ncid/BC16174712#entity
https://lccn.loc.gov/2022017949
Data Provider (Database): 国立情報学研究所 : CiNii Research
Original Data Provider (Database): CiNii Books
https://ci.nii.ac.jp/books
NCID: https://ci.nii.ac.jp/ncid/BC16174712 : BC16174712
LC Control Number: 2022017949
https://lccn.loc.gov/2022017949