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With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA’s recent draft guidance, a new edition of one of the first books on the topic is needed.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 Pharmazie
• 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

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.