"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 formsthe 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 clinicaltrials. 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!"--
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 grad students.
Arvustused
"This book is a must for someone that wants to work with the aforementioned models using SAS and wants a step-by-step guide on how and when to implement those models. Each chapter is organized in a very similar manner... It is one of the best books on applied statistics I have read up to this point. I am sure you will find it great as well if you are part of the intended target audience, as I have described above. Particularly for non-statisticians that have an upcoming analysis where linear regression or ANOVA models are planned, the book is a must in order to make sure the proper method is used, what to check, what alternatives there are and how to properly read and interpret the results when using SAS."
David Manteigas, Portugal, ISCB News, May 2024.
1. Bayesian Statistics and Adaptive Designs. 1.1. Basics of Bayesian
Statistics. 1.2. Bayesian Adaptive Designs. 1.2. Bayesian Adaptive Designs.
2. Algorithm and Model-Based Dose Finding Designs. 2.1 Introduction. 2.2.
Traditional 3+3 Design. 2.3. Cohort Expansion. 2.4 Accelerated Titration
Design. 2.5. Continual Reassessment Method. 2.6. Bayesian Model Averaging
CRM. 2.7. Escalation with Overdose Control. 2.8. Bayesian Logistic Regression
Method. 2.9. Software.
3. Model-Assisted Dose Finding Designs. 3.1.
Introduction. 3.2. Modified Toxicity Probability Interval Design. 3.3.
Keyboard Design. 3.4. 3.4 Bayesian Optimal Interval (BOIN) Design. 3.5.
Operating Characteristics. 3.6. Software and Case Study.
4. Drug-Combination
Trials. 4.1. Introduction. 4.2. Model-based Designs. 4.3. Model-assisted
Designs. 4.4. Operating Characteristics. 4.5. Software and Case Study.
5.
Late-Onset Toxicity. 5.1. A Common Logistical Problem. 5.2. Late-Onset
Toxicities. 5.3. TITE-CRM. 5.4. TITE-BOIN. 5.5. A Unified Approach using
"Effective" Data. 5.6. TITE-keyboard and TITE-mTPI designs. 5.7. Software and
Case Study.
6. Incorporating Historical Data. 6.1. Historical Data and Prior
Information. 6.2. Informative BOIN (iBOIN). 6.3. iKeyboard Design. 6.4.
Operating Characteristics. 6.5. Software and Case Study.
7. Multiple Toxicity
Grades. 7.1. Multiple Toxicity Grades. 7.2. gBOIN Accounting for Toxicity
Grade. 7.3. Multiple-toxicity BOIN. 7.4. Software and Illustration.
8.
Finding Optimal Biological Dose. 8.1. Introduction. 8.2. EffTox design. 8.3.
U-BOIN Design. 8.4. BOIN12 Design. 8.5. TITE-BOIN12 Design. 8.6. Other
Model-Assisted Phase I/II Designs. 8.7. Software and Case Study.
Bibliography. Index.
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. Yuans and Dr. J. Jack Lees team (www.trialdesign.org) have been widely used in medical research institutes and pharmaceutical companies. The BOIN design developed by Dr. Yuans 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 the American 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.
