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

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

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.