Muutke küpsiste eelistusi

E-raamat: Bayesian Adaptive Methods for Clinical Trials

(The University of Texas M.D. Anderson Cancer Center, Houston, USA), (The University of Texas M.D. Anderson Cancer Center, Houston, USA), (University of Minnesota, Minneapolis, USA), (Berry Consultants, College Station, Texas, USA)
  • Formaat - EPUB+DRM
  • Hind: 62,39 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimers disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.

The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase IIIII trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis.

For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites.

Scott Berry talks about the book on the CRC Press YouTube Channel.

Arvustused

researchers/statisticians who work in oncology clinical trials would especially benefit from this book. Having said that, much of the book should be accessible to nonstatistician readers with interest/involvement in clinical trials across different disease areas, while even Bayesian medical statistician readers should find the book to be a helpful resource. The authors, while clearly advocating the use of Bayesian approaches, nevertheless take a very pragmatic approach to the issue. They argue for Bayesian methods that demonstrate good frequentist properties and that are practical to use. This is a refreshing change from some other books and papers that also advocate Bayesian methods, but which while theoretically interesting, are difficult to implement in practice. In summary, I found this book to be well written and interesting. It is very timely given the interest in adaptive designs, and should be a useful resource for statisticians and nonstatisticians alike interested in adaptive clinical trials from a Bayesian perspective. Biometrics, 67, September 2011

This is the first serious text on adaptive designs using the Bayesian approach. This is the right book to get if you are interested in Bayesian methods for adaptive designs. Michael R. Chernick, Technometrics, August 2011

a rich introduction to Bayesian clinical trial design in general. It is written in an extremely readable style and is furnished with numerous examples and a great deal of helpful supplementary code. This work provides a good overall look at the Bayesian approach to clinical trials. It covers the theoretical framework, provides software for the many excellent examples, and even delves into the practical regulatory issues that arise with the use of the designs. The book would be a worthy addition to the practicing statisticians library. Journal of Statistical Software, November 2010, Volume 37

This fine book represents the most recent and exciting developments in this area, and gives ample justification for the power and elegance of Bayesian trial design and analysis. This book, based on the many years of cumulative experience of the authors, manages to deal with [ ideological, bureaucratic, practical and pragmatic] difficulties. Adaptive studies are a perfect application for a Bayesian approach, and I am confident that this book will be a major contribution to the science and practice of clinical trials. From the Foreword by David J. Spiegelhalter, MRC Biostatistics Unit, University of Cambridge, UK

"This excellent book can be recommended to any biostatistician with a professional interest in clinical trials, who has mainly applied frequentist methods up to now and who wants to gain a better understanding of the Bayesian methodology. The book can also be recommended to clinical investigators and regulators who have a minimum level of formal training. The practice-oriented and real-problem-solving approach of the book shows the strengths, potentials, difficulties, and yes, also weaknesses of the Bayesian adaptive approach for drug and medical device development. The book is well written, contains a lot of real life examples, and provides web links to software code (WinBUGS and R). In short, it is a fine practical handbook." Harald Heinzl, Zentralblatt MATH 1306

Foreword xi
Preface xiii
1 Statistical approaches for clinical trials
1(18)
1.1 Introduction
1(3)
1.2 Comparisons between Bayesian and frequentist approaches
4(2)
1.3 Adaptivity in clinical trials
6(2)
1.4 Features and use of the Bayesian adaptive approach
8(11)
1.4.1 The fully Bayesian approach
8(2)
1.4.2 Bayes as a frequentist tool
10(2)
1.4.3 Examples of the Bayesian approach to drug and medical device development
12(7)
2 Basics of Bayesian inference
19(68)
2.1 Introduction to Bayes' Theorem
19(7)
2.2 Bayesian inference
26(16)
2.2.1 Point estimation
26(1)
2.2.2 Interval estimation
27(2)
2.2.3 Hypothesis testing and model choice
29(5)
2.2.4 Prediction
34(3)
2.2.5 Effect of the prior: sensitivity analysis
37(1)
2.2.6 Role of randomization
38(2)
2.2.7 Handling multiplicities
40(2)
2.3 Bayesian computation
42(9)
2.3.1 The Gibbs sampler
44(1)
2.3.2 The Metropolis-Hastings algorithm
45(3)
2.3.3 Convergence diagnosis
48(1)
2.3.4 Variance estimation
49(2)
2.4 Hierarchical modeling and metaanalysis
51(12)
2.5 Principles of Bayesian clinical trial design
63(23)
2.5.1 Bayesian predictive probability methods
64(2)
2.5.2 Bayesian indifference zone methods
66(2)
2.5.3 Prior determination
68(2)
2.5.4 Operating characteristics
70(8)
2.5.5 Incorporating costs
78(3)
2.5.6 Delayed response
81(1)
2.5.7 Noncompliance and causal modeling
82(4)
2.6 Appendix: R Macros
86(1)
3 Phase I studies
87(50)
3.1 Rule-based designs for determining the MTD
88(5)
3.1.1 Traditional 3+3 design
88(3)
3.1.2 Pharmacologically guided dose escalation
91(1)
3.1.3 Accelerated titration designs
92(1)
3.1.4 Other rule-based designs
92(1)
3.1.5 Summary of rule-based designs
92(1)
3.2 Model-based designs for determining the MTD
93(23)
3.2.1 Continual reassessment method (CRM)
94(8)
3.2.2 Escalation with overdose control (EWOC)
102(3)
3.2.3 Time-to-event (TITE) monitoring
105(4)
3.2.4 Toxicity intervals
109(4)
3.2.5 Ordinal toxicity intervals
113(3)
3.3 Efficacy versus toxicity
116(5)
3.3.1 Trial parameters
117(1)
3.3.2 Joint probability model for efficacy and toxicity
117(1)
3.3.3 Defining the acceptable dose levels
118(1)
3.3.4 Efficacy-toxicity trade-off contours
118(3)
3.4 Combination therapy
121(13)
3.4.1 Basic Gumbel model
122(4)
3.4.2 Bivariate CRM
126(1)
3.4.3 Combination therapy with bivariate response
127(2)
3.4.4 Dose escalation with two agents
129(5)
3.5 Appendix: R Macros
134(3)
4 Phase II studies
137(56)
4.1 Standard designs
137(5)
4.1.1 Phase IIA designs
138(2)
4.1.2 Phase IIB designs
140(2)
4.1.3 Limitations of traditional frequentist designs
142(1)
4.2 Predictive probability
142(8)
4.2.1 Definition and basic calculations for binary data
143(3)
4.2.2 Derivation of the predictive process design
146(4)
4.3 Sequential stopping
150(5)
4.3.1 Binary stopping for futility and efficacy
150(1)
4.3.2 Binary stopping for futility, efficacy, and toxicity
151(3)
4.3.3 Monitoring event times
154(1)
4.4 Adaptive randomization and dose allocation
155(18)
4.4.1 Principles of adaptive randomization
155(8)
4.4.2 Dose ranging and optimal biologic dosing
163(4)
4.4.3 Adaptive randomization in dose finding
167(1)
4.4.4 Outcome adaptive randomization with delayed survival response
168(5)
4.5 Hierarchical models for phase II designs
173(3)
4.6 Decision theoretic designs
176(7)
4.6.1 Utility functions and their specification
176(3)
4.6.2 Screening designs for drug development
179(4)
4.7 Case studies in phase II adaptive design
183(8)
4.7.1 The BATTLE trial
183(6)
4.7.2 The I-SPY 2 trial
189(2)
4.8 Appendix: R Macros
191(2)
5 Phase III studies
193(56)
5.1 Introduction to confirmatory studies
193(2)
5.2 Bayesian adaptive confirmatory trials
195(13)
5.2.1 Adaptive sample size using posterior probabilities
196(4)
5.2.2 Futility analyses using predictive probabilities
200(4)
5.2.3 Handling delayed outcomes
204(4)
5.3 Arm dropping
208(3)
5.4 Modeling and prediction
211(7)
5.5 Prior distributions and the paradigm clash
218(3)
5.6 Phase III cancer trials
221(7)
5.7 Phase II/III seamless trials
228(13)
5.7.1 Example phase II/III trial
230(1)
5.7.2 Adaptive design
231(1)
5.7.3 Statistical modeling
232(1)
5.7.4 Calculation
233(2)
5.7.5 Simulations
235(6)
5.8 Case study: Ablation device to treat atrial fibrillation
241(6)
5.9 Appendix: R Macros
247(2)
6 Special topics
249(32)
6.1 Incorporating historical data
249(11)
6.1.1 Standard hierarchical models
250(2)
6.1.2 Hierarchical power prior models
252(8)
6.2 Equivalence studies
260(8)
6.2.1 Statistical issues in bioequivalence
261(2)
6.2.2 Binomial response design
263(2)
6.2.3 2 x 2 crossover design
265(3)
6.3 Multiplicity
268(8)
6.3.1 Assessing drug safety
269(6)
6.3.2 Multiplicities and false discovery rate (FDR)
275(1)
6.4 Subgroup analysis
276(4)
6.4.1 Bayesian approach
276(1)
6.4.2 Bayesian decision theoretic approach
277(3)
6.5 Appendix: R Macros
280(1)
References 281(16)
Author index 297(6)
Index 303
Scott M. Berry is the President and Senior Statistical Scientist at Berry Consultants, a statistical consulting group specializing in adaptive clinical trial design in pharmaceutical and medical device research and development.

Bradley P. Carlin is Mayo Professor in Public Health and Head of the Division of Biostatistics at the University of Minnesota.

J. Jack Lee is Professor of Biostatistics at the University of Texas M.D. Anderson Cancer Center.

Peter Müller is a Robert R. Herring Distinguished Professor in Clinical Research in the Department of Biostatistics at the University of Texas M.D. Anderson Cancer Center.