Chang Introductory Adaptive Trial Designs

A Practical Guide with R
1. Auflage 2015
ISBN: 978-1-4987-1747-2
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 232 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

All the Essentials to Start Using Adaptive Designs in No Time

Compared to traditional clinical trial designs, adaptive designs often lead to increased success rates in drug development at reduced costs and time. Introductory Adaptive Trial Designs: A Practical Guide with R motivates newcomers to quickly and easily grasp the essence of adaptive designs as well as the foundations of adaptive design methods.

The book reduces the mathematics to a minimum and makes the material as practical as possible. Instead of providing general, black-box commercial software packages, the author includes open-source R functions that enable readers to better understand the algorithms and customize the designs to meet their needs. Readers can run the simulations for all the examples and change the input parameters to see how each input parameter affects the simulation outcomes or design operating characteristics.

Taking a learning-by-doing approach, this tutorial-style book guides readers on planning and executing various types of adaptive designs. It helps them develop the skills to begin using the designs immediately.

Autoren/Hrsg.

Chang, Mark

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Introduction
Motivation
Adaptive Designs in Clinical Trials
Clinical Trial Simulation
Characteristics of Adaptive Designs
FAQs about Adaptive Designs

Classical Design
Introduction
Two-Group Superiority
Two-Group Noninferiority Trial
Two-Group Equivalence Trial
Trial with Any Number of Groups
Multigroup Dose-Finding Trial
Summary and Discussion

Two-Stage Adaptive Confirmatory Design Method
General Formulation
Method Based on Sum of p-Values
Method with Product of p-Values
Method with Inverse-Normal p-Values
Comparisons of Adaptive Design Methods

K-Stage Adaptive Confirmatory Design Methods
Test Statistics
Determination of Stopping Boundary
Error-Spending Function
Power and Sample Size
Error Spending Approach

Sample-Size Reestimation Design
Sample Size Reestimation Methods
Comparisons of SSR Methods
K-Stage Sample Size Reestimaion Trial
Summary

Special Two-Stage Group Sequential Trials
Event-Based Design
Equivalence Trial
Adaptive Design with Farrington-Manning Margin
Noninferiority Trial with Paired Binary Data
Trial with Incomplete Paired Data
Trial with Coprimary Endpoints
Trial with Multiple Endpoints

Pick-the-Winners Design
Overview of Multiple-Arm Designs
Pick-the-Winner Design
Stopping Boundary and Sample Size
Summary and Discussion

The Add-Arms Design
Introduction
The Add-Arm Design
Clinical Trial Examples
Extension of Add-Arms Designs
Summary

Biomarker-Adaptive Design
Taxonomy
Biomarker-Enrichment Design
Biomarker-Informed Adaptive Design
Summary

Response-Adaptive Randomization
Basic Response-Adaptive Randomizations
Generalized Response-Adaptive Randomization
Summary and Discussion

Adaptive Dose-Escalation Trial
Oncology Dose-Escalation Trial
Continual Reassessment Method
Alternative Form CRM
Evaluation of Dose-Escalation Design
Summary and Discussion

Deciding Which Adaptive Design to Use
Determining the Objectives
Determining Design Parameters
Evaluation Matrix of Adaptive Design

Monitoring Trials and Making Adaptations
Stopping and Arm-Selection
Conditional Power
Sample-Size Reestimation
New Randomization Scheme

Data Analyses of Adaptive Trials
Orderings in Sample Space
Adjusted p-Value
Parameter Estimation
Confidence Interval
Summary

Planning and Execution
Study Planning
Working with a Regulatory Agency
Trial Execution
Summary

Appendix A: Thirty-Minute Tutorial to R
Appendix B: R Functions for Adaptive Designs

Bibliography

Index

Über Autor(innen)

Mark Chang is vice president of biometrics at AMAG Pharmaceuticals and an adjunct professor at Boston University. Dr. Chang is an elected fellow of the American Statistical Association and a co-founder of the International Society for Biopharmaceutical Statistics. He serves on the editorial boards of statistical journals and has published eight books, including Principles of Scientific Methods, Paradoxes in Scientific Inference, Modern Issues and Methods in Biostatistics, Monte Carlo Simulation for the Pharmaceutical Industry, and Adaptive Design Theory and Implementation Using SAS and R, Second Edition.