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Clinical Trial Design: Bayesian and Frequentist Adaptive Methods [NOOK Book]

Overview

A balanced treatment of the theories, methodologies, and design issues involved in clinical trials using statistical methods

There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. However, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. From practical perspectives, Clinical Trial Design: Bayesian and ...

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Clinical Trial Design: Bayesian and Frequentist Adaptive Methods

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Overview

A balanced treatment of the theories, methodologies, and design issues involved in clinical trials using statistical methods

There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. However, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. From practical perspectives, Clinical Trial Design: Bayesian and Frequentist Adaptive Methods provides comprehensive coverage of both Bayesian and frequentist approaches to all phases of clinical trial design. Before underpinning various adaptive methods, the book establishes an overview of the fundamentals of clinical trials as well as a comparison of Bayesian and frequentist statistics.

Recognizing that clinical trial design is one of the most important and useful skills in the pharmaceutical industry, this book provides detailed discussions on a variety of statistical designs, their properties, and operating characteristics for phase I, II, and III clinical trials as well as an introduction to phase IV trials. Many practical issues and challenges arising in clinical trials are addressed. Additional topics of coverage include:

  • Risk and benefit analysis for toxicity and efficacy trade-offs
  • Bayesian predictive probability trial monitoring
  • Bayesian adaptive randomization
  • Late onset toxicity and response
  • Dose finding in drug combination trials
  • Targeted therapy designs

The author utilizes cutting-edge clinical trial designs and statistical methods that have been employed at the world's leading medical centers as well as in the pharmaceutical industry. The software used throughout the book is freely available on the book's related website, equipping readers with the necessary tools for designing clinical trials.

Clinical Trial Design is an excellent book for courses on the topic at the graduate level. The book also serves as a valuable reference for statisticians and biostatisticians in the pharmaceutical industry as well as for researchers and practitioners who design, conduct, and monitor clinical trials in their everyday work.

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Editorial Reviews

From the Publisher
“In summary, this book is useful for anyone interested in design and analysis of clinical studies. The main strengths of this book are the well-balanced approach between statistical theories and statistical analysis methods of the design issues involved in clinical trials. I would suggest the usage of this book as a Master’s level course in biostatistics.” (Journal of Biopharmaceutical Statistics, 1 May 2013)

“The author should be commended; the text is well written and packed with information that belies the book’s trim size." (Drug Information Journal, 30 October 2012)

“The book accompanied with software developed at MD Anderson Cancer Center provides an excellent reference for everyone who works in clinical trial field.” (Biometrics, 1 July 2012)

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Product Details

Meet the Author

GUOSHENG YIN, PhD, is Associate Professor in the Department of Statistics and Actuarial Science at The University of Hong Kong, and Adjunct Associate Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center.

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Table of Contents

Preface xv

1. Introduction 1

1.1 What Are Clinical Trials? 1

1.2 Brief History and Adaptive Designs 3

1.3 Modern Clinical Trials 7

1.4 Different Types of Drugs 12

1.5 New Drug Development 13

1.6 Emerging Challenges 16

1.7 Summary 17

2. Fundamentals of Clinical Trials 21

2.1 Key Components of Clinical Trials 21

2.2 Pharmacokinetics and Pharmacodynamics 35

2.3 Phases I-IV of Clinical Trials 38

2.4 Summary 42

3. Frequentist versus Bayesian Statistics 45

3.1 Basic Statistics 45

3.2 Frequentist Estimation and Inference 62

3.3 Survival Analysis 77

3.4 Bayesian Methods 86

3.5 Markov Chain Monte Carlo 105

3.6 Summary 109

4. Phase I Trial Design 113

4.1 Maximum Tolerated Dose 113

4.2 Initial Dose and Spacing 116

4.3 3 + 3 Design 120

4.4 A + B Design 125

4.5 Accelerated Titration Design 126

4.6 Biased Coin Dose-Finding Method 130

4.7 Continual Reassessment Method 132

4.8 Bayesian Model Averaging Continual Reassessment Method 140

4.9 Escalation with Overdose Control 152

4.10 Bayesian Hybrid Design Using Bayes Factor 155

4.11 Summary 162

5. Phase II Trial Design 169

5.1 Gehan’s Two-Stage Design 173

5.2 Simon’s Two-Stage Design 175

5.3 Bayesian Posterior Probability Monitoring 179

5.4 Bayesian Predictive Probability Monitoring 183

5.5 Predictive Monitoring in Randomized Phase II Trials 186

5.6 Predictive Probability with Adaptive Randomization 191

5.7 Phase II Design with Multiple Outcomes 198

5.8 Phase I/II Design with Bivariate Binary Data 206

5.9 Phase I/II Design with Times to Toxicity and Efficacy 218

5.10 Summary 229

6. Phase III Trial Design 233

6.1 Power and Sample Size 233

6.2 Comparing Means for Continuous Outcomes 240

6.3 Comparing Proportions for Binary Outcomes 252

6.4 Sample Size with Survival Data 262

6.5 Sample Size for Correlated Data 270

6.6 Group Sequential Methods 274

6.7 Adaptive Designs 297

6.8 Causality and Noncompliance 310

6.9 Phase IV Post-Approval Trial 317

7. Adaptive Randomization 323

7.1 Introduction 323

7.2 Simple Randomization 326

7.3 Permuted Block Randomization 327

7.4 Stratified Randomization 328

7.5 Covariate-Adaptive Allocation by Minimization 329

7.6 Biased Coin Design 333

7.7 Play-the-Winner Rule 335

7.8 Drop-the-Loser Rule 338

7.9 Optimal Adaptive Randomization 339

7.10 Doubly Adaptive Biased Coin Design 346

7.11 Bayesian Adaptive Randomization 347

7.12 Adaptive Randomization with Efficacy and Toxicity Trade-offs 356

7.13 Fixed or Adaptive Randomization? 360

8. Late-Onset Toxicity 367

8.1 Introduction 367

8.2 Missing Data Caused by Delayed Outcomes 369

8.3 Fractional 3 + 3 Design 371

8.4 Fractional Continual Reassessment Method 377

8.5 Time-to-Event Continual Reassessment Method 379

8.6 EM Continual Reassessment Method 382

9. Drug-Combination Trials 393

9.1 Why Are Drugs Combined? 393

9.2 New Challenges 397

9.3 Sequential Dose-Finding Scheme 402

9.4 Dose Finding with Copula-Type Regression 405

9.5 Latent Contingency Table Approach 414

9.6 Combination of Discrete and Continuous Doses 419

9.7 Phase I/II Drug-Combination Design 426

9.8 Summary 434

10. Targeted Therapy Design 437

10.1 Cytostatic Agent 437

10.2 Prognostic and Predictive Biomarkers 439

10.3 Predictive Biomarker Validation 441

10.4 Randomized Discontinuation Design 444

10.5 Adaptive Signature Design 447  

10.6 Adaptive Threshold Design 451

References 457

Author Index 476

Subject Index 480 

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