Model-assisted Bayesian designs for dose finding and optimization : methods and applications / Ying Yuan, Ruitao Lin, J. Jack Lee.
"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 succes...
Saved in:
| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Main Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
2023.
|
| Edition: | First edition. |
| Series: | Chapman & Hall/CRC biostatistics series.
|
| Subjects: |
MARC
| LEADER | 00000cam a2200000xi 4500 | ||
|---|---|---|---|
| 001 | b12805888 | ||
| 003 | CoU | ||
| 005 | 20250122164057.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 220616t20232023flua ob 001 0 eng | ||
| 010 | |a 2022017950 | ||
| 020 | |a 9780429052781 |q electronic book | ||
| 020 | |a 0429052782 |q electronic book | ||
| 020 | |a 9780429628474 |q electronic book | ||
| 020 | |a 0429628471 |q electronic book | ||
| 020 | |a 9780429626838 |q electronic book | ||
| 020 | |a 0429626835 |q electronic book | ||
| 020 | |z 9780367146245 |q hardcover | ||
| 020 | |z 9781032357126 |q paperback | ||
| 024 | 7 | |a 10.1201/9780429052781 | |
| 035 | |a (OCoLC)tfe1338166063 | ||
| 035 | |a (OCoLC)1338166063 | ||
| 037 | |a tfe9780429052781 | ||
| 040 | |a DLC |b eng |e rda |c DLC |d TYFRS |d OCLCF |d YDX | ||
| 042 | |a pcc | ||
| 049 | |a GWRE | ||
| 050 | 0 | 0 | |a RM301.25 |b .Y83 2023 |
| 100 | 1 | |a Yuan, Ying |c (Professor of biostatistics), |e author. |0 http://id.loc.gov/authorities/names/n2016012374 |1 http://isni.org/isni/0000000453969720. | |
| 245 | 1 | 0 | |a Model-assisted Bayesian designs for dose finding and optimization : |b methods and applications / |c Ying Yuan, Ruitao Lin, J. Jack Lee. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c 2023. | |
| 264 | 4 | |c ©2023. | |
| 300 | |a 1 online resource (xiii, 219 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a Chapman & Hall/CRC biostatistics series. | |
| 545 | 0 | |a Ying Yuan, Ph.D., is Bettyann Asche Murray Distinguished Professor in Biostatistics and Deputy Chair at the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. He has published over 100 statistical methodology papers on innovative Bayesian adaptive designs, including early phase trials, seamless trials, biomarker-guided trials, and basket and platform trials. The designs and software developed by Dr. Yuan's and Dr. J. Jack Lee's team (www.trialdesign.org) have been widely used in medical research institutes and pharmaceutical companies. The BOIN design developed by Dr. Yuan's team is the first oncology dose-finding design designated as a fit-for-purpose drug development tool by FDA. Dr. Yuan is an elected Fellow of theAmerican Statistical Association, and is a co-author of the book Bayesian Designs for Phase I-II Clinical Trials published by Chapman & Hall/CRC Press. Ruitao Lin, Ph.D., is an Assistant Professor in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. Motivated by the unmet need for the development of precision medicine, Dr. Lin has developed many innovative statistical designs to increase trial efficiency, optimize healthcare decisions, and expedite drug development. He made substantial contributions to generalize model-assisted designs, including BOIN, to handle combination trials, late-onset toxicity, and dose optimization. Dr. Lin has published over 40 papers in top statistical and medical journals. He currently is an Associate Editor of Biometrial Journal, Pharmaceutical Statistics, and Contemporary Clinical Trials. J. Jack Lee, Ph.D., is a Professor of Biostatistics, Kenedy Foundation Chair in Cancer Research, and Associate Vice President in Quantitative Sciences at the University of Texas MD Anderson Cancer Center. He is an expert on the design and analysis of Bayesian adaptive designs, platform trials, basket trials, umbrella trials, master protocols, statistical computation/graphics, drug combination studies, and biomarkers identification and validation. Dr. Lee has also been actively participating in basic, translational, and clinical cancer research in chemoprevention, immuno-oncology, and precision oncology. He is an elected Fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for the Advancement of Science. He is Statistical Editor of Cancer Prevention Research and serves on the Statistical Editorial Board of Journal of the National Cancer Institute. He has over 500 publications and is a co-author of the book Bayesian Adaptive Methods for Clinical Trials published by Chapman & Hall/CRC Press. | |
| 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. | |
| 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. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on January 09, 2023) | ||
| 650 | 0 | |a Drug development |x Statistical methods. | |
| 650 | 7 | |a Drug development |x Statistical methods. |2 fast |0 fst00898671 | |
| 700 | 1 | |a Lin, Ruitao, |e author. |0 http://id.loc.gov/authorities/names/n2022182436 | |
| 700 | 1 | |a Lee, J. Jack, |e author. |0 http://id.loc.gov/authorities/names/n2022182438 | |
| 776 | 0 | 8 | |i Print version: |a Yuan, Ying |t Model-assisted Bayesian designs for dose finding and optimization |b First edition. |d Boca Raton : C&Hall/CRC Press, 2023 |z 9780367146245 |w (DLC) 2022017949. |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://www.taylorfrancis.com/books/9780429052781 |z Full Text (via Taylor & Francis) |
| 830 | 0 | |a Chapman & Hall/CRC biostatistics series. |0 http://id.loc.gov/authorities/names/no2006134495 | |
| 907 | |a .b128058882 |b 02-01-23 |c 11-03-22 | ||
| 915 | |a - | ||
| 998 | |a web |b 01-31-23 |c b |d b |e - |f eng |g flu |h 0 |i 1 | ||
| 907 | |a .b128058882 |b 01-31-23 |c 11-03-22 | ||
| 944 | |a MARS - RDA ENRICHED | ||
| 956 | |a Taylor & Francis Ebooks | ||
| 956 | |b Taylor & Francis All eBooks | ||
| 999 | f | f | |i 529a191b-93a9-541e-938d-ba66bb49eb15 |s 68367c21-c0ab-56c2-8777-d647624f689c |
| 952 | f | f | |p Can circulate |a University of Colorado Boulder |b Online |c Online |d Online |e RM301.25 .Y83 2023 |h Library of Congress classification |i web |n 1 |