Tools

LEADER 05700nam 2200625 i 4500

001 ssj0002710104;

003 WaSeSS;

005 20251212090320.0;

006 m d ;

007 cr n ;

008 220616s2023 flu sb 001 0 eng d;

010 a| 2022017950;

020 a| 9780429052781q| (ebook);

020 z| 9780367146245q| (hardback);

020 z| 9781032357126q| (paperback);

035 a| (WaSeSS)ssj0002710104;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| RM301.25;

060 00 a| QV 745;

082 00 a| 615.1/92| 23/eng/20220801;

100 1 a| Yuan, Yingc| (Professor of biostatistics)?| UNAUTHORIZED;

245 10 a| Model-assisted Bayesian designs for dose finding and optimizationh| [electronic resource] :b| methods and applications /c| Ying Yuan, The University of Texas MD Anderson Cancer Center, USA, Ruitao Lin, The University of Texas MD Anderson Cancer Center, USA, J. Jack Lee, The University of Texas MD Anderson Cancer Center, USA.;

250 a| First edition.;

260 a| Boca Raton :b| CRC Press, Taylor & Francis Group,c| 2023.;

300 a| 1 online resource;

490 1 a| Chapman & Hall/CRC biostatistics series;

500 a| "A Chapman & Hall book.";

504 a| Includes bibliographical references and index.;

505 0 a| Bayesian Statistics and Adaptive Designs -- Algorithm and Model-Based Dose Finding Designs -- Model-Assisted Dose Finding Designs -- Drug-Combination Trials -- Late-Onset Toxicity -- Incorporating Historical Data -- Multiple Toxicity Grades -- Finding Optimal Biological Dose.;

506 a| Available only to authorized users.;

520 a| "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 FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges and issues arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustration of using software to design trials Develops a companion website (www.trialdesign.org) to provide easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

588 a| Description based on print version record and CIP data provided by publisher.;

650 12 a| Drug Developmentx| methods=| ^A1418906;

650 12 a| Drug Designx| methods=| ^A925973;

650 22 a| Drug Dosage Calculations=| ^A1037540;

650 22 a| Models, Statistical=| ^A927368;

650 22 a| Bayes Theorem=| ^A927397;

650 22 a| Clinical Trials, Phase I as Topic=| ^A1204662;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Lin, Ruitao.?| UNAUTHORIZED;

700 1 a| Lee, J.q| (Jack)?| UNAUTHORIZED;

830 0 a| Chapman & Hall/CRC biostatistics series.=| ^A707265;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9780429052781;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 6676791;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6676791-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6676791-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 12/19/2025x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6676791-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

Model-assisted Bayesian designs for dose finding and optimization methods and applications / Ying Yuan, The University of Texas MD Anderson Cancer Center, USA, Ruitao Lin, The University of Texas MD Anderson Cancer Center, USA, J. Jack Lee, The University of Texas MD Anderson Cancer Center, USA.

Author/creator	Yuan, Ying
Other author	Lin, Ruitao.
Other author	Lee, J. (Jack)
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton : CRC Press, Taylor & Francis Group, 2023.
Description	1 online resource
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Drug Development--methods. Drug Design--methods. Drug Dosage Calculations Models, Statistical Bayes Theorem Clinical Trials, Phase I as Topic

Series	Chapman & Hall/CRC biostatistics series Chapman & Hall/CRC biostatistics series. ^A707265
Contents	Bayesian Statistics and Adaptive Designs -- Algorithm and Model-Based Dose Finding Designs -- Model-Assisted Dose Finding Designs -- Drug-Combination Trials -- Late-Onset Toxicity -- Incorporating Historical Data -- Multiple Toxicity Grades -- Finding Optimal Biological Dose.
Abstract	"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 FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges and issues arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustration of using software to design trials Develops a companion website (www.trialdesign.org) to provide easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!"-- Provided by publisher.
General note	"A Chapman & Hall book."
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Source of description	Description based on print version record and CIP data provided by publisher.
Issued in other form	Print version: Yuan, Ying (Professor of biostatistics). Model-assisted Bayesian designs for dose finding and optimization First edition. Boca Raton : CRC Press, Taylor & Francis Group, 2023 9780367146245
Genre/form	Electronic books.
LCCN	2022017950
ISBN	9780429052781 (ebook)
ISBN	(hardback)
ISBN	(paperback)

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Model-assisted Bayesian designs for dose finding and optimization methods and applications / Ying Yuan, The University of Texas MD Anderson Cancer Center, USA, Ruitao Lin, The University of Texas MD Anderson Cancer Center, USA, J. Jack Lee, The University of Texas MD Anderson Cancer Center, USA.

Click here for more information about this title

Availability