You are previewing Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods.
O'Reilly logo
Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

Book Description

Get the tools you need to use SAS® in clinical trial design! Unique and multifaceted, Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods, edited by Sandeep M. Menon and Richard C. Zink, thoroughly covers several domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods that are applicable to and widely used in various phases of pharmaceutical development. Written for biostatisticians, pharmacometricians, clinical developers, and statistical programmers involved in the design, analysis, and interpretation of clinical trials, as well as students in graduate and postgraduate programs in statistics or biostatistics, the book touches on a wide variety of topics, including dose-response and dose-escalation designs; sequential methods to stop trials early for overwhelming efficacy, safety, or futility; Bayesian designs that incorporate historical data; adaptive sample size re-estimation; adaptive randomization to allocate subjects to more effective treatments; and population enrichment designs. Methods are illustrated using clinical trials from diverse therapeutic areas, including dermatology, endocrinology, infectious disease, neurology, oncology, and rheumatology. Individual chapters are authored by renowned contributors, experts, and key opinion leaders from the pharmaceutical/medical device industry or academia. Numerous real-world examples and sample SAS code enable users to readily apply novel clinical trial design and analysis methodologies in practice.

Table of Contents

  1. Title Page
  2. Copyright
  3. Foreword
  4. About This Book
  5. About the Author
  6. Acknowledgments
  7. Chapter 1: Overview of Clinical Trials in Support of Drug Development
    1. 1.1 Introduction
    2. 1.2 Evolution of Clinical Trials and the Emergence of Guidance Documents
    3. 1.3 Emergence of Group Sequential Designs in the 70s and 80s
    4. 1.4 Emergence of Adaptive Designs in the 90s
    5. 1.5 Widespread Research on Adaptive Designs Since the Turn of the 21st Century
    6. 1.6 Opportunities and Challenges in Designing, Conducting, and Analyzing Adaptive Trials
    7. 1.7 The Future of Adaptive Trials in Clinical Drug Development
    8. References
    9. Authors
  8. Chapter 2: Designing and Monitoring Group Sequential Clinical Trials
    1. 2.1 Introduction
    2. 2.2 Examples of Classical Fixed-Sample Designs
    3. 2.3 Theories of Group Sequential Tests
    4. 2.4 Types of Stopping Boundaries
    5. 2.5 Special Issues
    6. 2.6 Summary
    7. References
    8. Authors
  9. Chapter 3: Sample Size Re-estimation
    1. 3.1 Introduction
    2. 3.2 Blinded SSR Methods
    3. 3.3 Unblinded SSR Methods
    4. 3.4 Information-Based Design
    5. 3.5 Summary and Conclusions
    6. References
    7. Authors
  10. Chapter 4: Bayesian Survival Meta-Experimental Design Using Historical Data
    1. 4.1 Introduction
    2. 4.2 Meta Design Setting
    3. 4.3 Meta-Regression Survival Models
    4. 4.4 Bayesian Meta-Experimental Design
    5. 4.5 Specification of Prior Distributions
    6. 4.6 Computational Algorithms
    7. 4.7 SAS MACRO BSMED
    8. 4.8 Summary
    9. References
    10. Authors
    11. Appendix
  11. Chapter 5: Continual Reassessment Methods
    1. 5.1 Dose Finding in Oncology
    2. 5.2 Continual Reassessment Method
    3. 5.3 Bayesian Model Averaging Continual Reassessment Method
    4. 5.4 Fractional Continual Reassessment Method
    5. 5.5 Time-to-Event Continual Reassessment Method
    6. 5.6 Summary
    7. References
    8. Authors
  12. Chapter 6: Classical Dose-Response Study
    1. 6.1 Introduction
    2. 6.2 Statistical Design Considerations in a Classical Dose-Response Study
    3. 6.3 Considerations in the Design and Final Analysis Stages
    4. 6.4 Analysis Examples Using Simulated Data
    5. 6.5 Summary
    6. References
    7. Authors
  13. Chapter 7: Implementing the MCP-Mod Procedure for Dose-Response Testing and Estimation
    1. 7.1 Introduction
    2. 7.2 Methodology
    3. 7.3 Considerations for MCP-Mod at the Design Stage
    4. 7.4 Further Considerations on MCP-Mod
    5. 7.5 Analysis Examples Using SAS
    6. 7.6 Conclusions
    7. References
    8. Authors
    9. Appendix
  14. Chapter 8: Bayesian Dose Response
    1. 8.1 Introduction and Background
    2. 8.2 Statistical Model for Bayesian Dose-Response
    3. 8.3 Analysis Examples for a Continuous Endpoint
    4. 8.4 Analysis Example for a Binary Endpoint
    5. 8.5 Summary
    6. References
    7. Authors
  15. Chapter 9: Overview of Adaptive Randomization
    1. 9.1 Introduction
    2. 9.2 Simple Randomization
    3. 9.3 Restricted Randomization
    4. 9.4 Covariate-Adaptive Randomization
    5. 9.5 Response-Adaptive Randomization
    6. 9.6 Summary
    7. References
    8. Authors
  16. Chapter 10: Optimal Response-Adaptive Randomization Designs in Binary Outcome Clinical Trials
    1. 10.1 Introduction
    2. 10.2 Optimal Allocation
    3. 10.3 Response-Adaptive Randomization for Implementing Optimal Allocation
    4. 10.4 Simulation of Optimal Response-Adaptive Randomization Procedures
    5. 10.5 Power and Sample Size for Response-Adaptive Randomization
    6. 10.6 Additional Considerations
    7. 10.7 Examples
    8. 10.8 Summary
    9. References
    10. Authors
  17. Chapter 11: Population Enrichment Designs
    1. 11.1 Introduction
    2. 11.2 Classical Designs
    3. 11.3 Efficiency of Classical Enrichment Designs
    4. 11.4 Adaptive Enrichment Designs
    5. 11.5 Summary
    6. References
    7. Authors
  18. Index
  19. Additional Resources