Chow / Chang Adaptive Design Methods in Clinical Trials, Second Edition

2. Auflage 2012
ISBN: 978-1-4398-3988-1
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 374 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA’s recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clinical trials. It unifies the vast and continuously growing literature and research activities on regulatory requirements, scientific and practical issues, and statistical methodology.
New to the Second Edition
Along with revisions throughout the text, this edition significantly updates the chapters on protocol amendment and clinical trial simulation to incorporate the latest changes. It also includes five entirely new chapters on two-stage adaptive design, biomarker adaptive trials, target clinical trials, sample size and power estimation, and regulatory perspectives.

Following in the tradition of its acclaimed predecessor, this second edition continues to offer an up-to-date resource for clinical scientists and researchers in academia, regulatory agencies, and the pharmaceutical industry. Written in an intuitive style at a basic mathematical and statistical level, the book maintains its practical approach with an emphasis on concepts via numerous examples and illustrations.

Zielgruppe

Statisticians, clinicians, physicians, biostatisticians, pharmaceutical scientists, and others involved with clinical trials.

Autoren/Hrsg.

Chow, Shein-Chung

Chang, Mark

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Pharmazie

Inhaltsverzeichnis

Introduction
What Is Adaptive Design
Regulatory Perspectives
Target Patient Population
Statistical Inference
Practical Issues
Aims and Scope of the Book

Protocol Amendment
Introduction
Moving Target Patient Population
Analysis with Covariate Adjustment
Assessment of Sensitivity Index
Sample Size Adjustment
Concluding Remarks

Adaptive Randomization
Conventional Randomization
Treatment-Adaptive Randomization
Covariate-Adaptive Randomization
Response-Adaptive Randomization
Issues with Adaptive Randomization
Summary

Adaptive Hypotheses
Modifications of Hypotheses
Switch from Superiority to Noninferiority
Concluding Remarks

Adaptive Dose-Escalation Trials
Introduction
CRM in Phase I Oncology Study
Hybrid Frequentist-Bayesian Adaptive Design
Design Selection and Sample Size
Concluding Remarks

Adaptive Group Sequential Design
Sequential Methods
General Approach for Group Sequential Design
Early Stopping Boundaries
Alpha Spending Function
Group Sequential Design Based on Independent P-Values
Calculation of Stopping Boundaries
Group Sequential Trial Monitoring
Conditional Power
Practical Issues

Statistical Tests for Seamless Adaptive Designs
Why a Seamless Design Is Efficient
Step-Wise Test and Adaptive Procedures
Contrast Test and Naive P-Value
Comparisons of Seamless Design
Drop-the-Loser Adaptive Design
Summary

Adaptive Sample Size Adjustment
Sample Size Re-Estimation without Unblinding Data
Cui-Hung-Wang’s Method
Proschan-Hunsberger’s Method
Müller-Schafer Method
Bauer-Köhne Method
Generalization of Independent P-Value Approaches
Inverse-Normal Method
Concluding Remarks

Two-Stage Adaptive Design
Introduction
Practical Issues
Types of Two-Stage Adaptive Designs
Analysis for Seamless Design with Same Study Objectives/Endpoints
Analysis for Seamless Design with Different Endpoints
Analysis for Seamless Design with Different Objectives/Endpoints
Concluding Remarks

Adaptive Treatment Switching
Latent Event Times
Proportional Hazard Model with Latent Hazard Rate
Mixed Exponential Model
Concluding Remarks

Bayesian Approach
Basic Concepts of Bayesian Approach
Multiple-Stage Design for Single-Arm Trial
Bayesian Optimal Adaptive Designs
Concluding Remarks

Biomarker Adaptive Trials
Introduction
Types of Biomarkers and Validation
Design with Classifier Biomarker
Adaptive Design with Prognostic Biomarker
Adaptive Design with Predictive Marker
Concluding Remarks
Appendix

Target Clinical Trials
Introduction
Potential Impact and Significance
Evaluation of Treatment Effect
Other Study Designs and Models
Concluding Remarks

Sample Size and Power Estimation
Framework and Model/Parameter Assumptions
Method Based on the Sum of P-Values
Method Based on Product of P-Values
Method with Inverse-Normal P-Values
Sample Size Re-Estimation
Summary

Clinical Trial Simulation
Introduction
Software Application of ExpDesign Studio
Early Phases Development
Late Phases Development
Concluding Remarks

Regulatory Perspectives — A Review of FDA Draft Guidance
Introduction
The FDA Draft Guidance
Well-Understood Designs
Less Well-Understood Designs
Adaptive Design Implementation
Concluding Remarks

Case Studies
Basic Considerations
Adaptive Group Sequential Design
Adaptive Dose-Escalation Design
Two-Stage Phase II/III Adaptive Design

Bibliography
Subject Index

Über Autor(innen)

Shein-Chung Chow is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also an adjunct professor of clinical sciences at Duke–National University of Singapore Graduate Medical School and the editor-in-chief of the Journal of Biopharmaceutical Statistics. He has authored or co-authored numerous papers and books, including the Handbook of Adaptive Designs in Pharmaceutical and Clinical Development and Controversial Statistical Issues in Clinical Trials.
Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals and an adjunct professor at Boston University. A fellow of the American Statistical Association, Dr. Chang is a co-founder of the International Society for Biopharmaceutical Statistics and serves on the editorial boards of two statistical journals. He has authored many publications, including Adaptive Design Theory and Implementation Using SAS and R and Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies.