Chang Adaptive Design Theory and Implementation Using SAS and R

Erscheinungsjahr 2012
ISBN: 978-1-58488-963-2
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 440 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

Adaptive design has become an important tool in modern pharmaceutical research and development. Compared to a classic trial design with static features, an adaptive design allows for the modification of the characteristics of ongoing trials based on cumulative information. Adaptive designs increase the probability of success, reduce costs and the time to market, and promote accurate drug delivery to patients.

Reflecting the state of the art in adaptive design approaches, Adaptive Design Theory and Implementation Using SAS and R provides a concise, unified presentation of adaptive design theories, uses SAS and R for the design and simulation of adaptive trials, and illustrates how to master different adaptive designs through real-world examples. The book focuses on simple two-stage adaptive designs with sample size re-estimation before moving on to explore more challenging designs and issues that include drop-loser, adaptive dose-funding, biomarker-adaptive, multiple-endpoint adaptive, response-adaptive randomization, and Bayesian adaptive designs. In many of the chapters, the author compares methods and provides practical examples of the designs, including those used in oncology, cardiovascular, and inflammation trials.

Equipped with the knowledge of adaptive design presented in this book, you will be able to improve the efficiency of your trial design, thereby reducing the time and cost of drug development.

Zielgruppe

Statisticians in the pharmaceutical industry and clinical research; researchers, practitioners, and students of statistics.

Autoren/Hrsg.

Chang, Mark

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Pharmazeutische Technologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

PREFACE

INTRODUCTION

Motivation

Adaptive Design Methods in Clinical Trials

FAQs about Adaptive Designs

Road Map

Classic Design

Overview of Drug Development

Two-Group Superiority and Noninferiority Designs

Two-Group Equivalence Trial

Dose-Response Trials

Maximum Information Design

Theory of Adaptive Design

Introduction

General Theory

Design Evaluation-Operating Characteristics

Method with Direct Combination of P-values

Method Based on Individual P-Values

Method Based on the Sum of P-Values

Method with Linear Combination of P-Values

Method with Product of P-Values

Event-Based Adaptive Design

Adaptive Design for Equivalence Trial

Method with Inverse-Normal P-values

Method with Linear Combination of Z-Scores

Lehmacher and Wassmer Method

Classic Group Sequential Method

Cui–Hung–Wang Method

Lan–DeMets Method

Fisher–Shen Method

Implementation of K-Stage Adaptive Designs

Introduction

Nonparametric Approach

Error-Spending Approach

Conditional Error Function Method

Proschan–Hunsberger Method

Denne Method

Müller–Schäfer Method

Comparison of Conditional Power

Adaptive Futility Design

Recursive Adaptive Design

P-Clud Distribution

Two-Stage Design

Error-Spending and Conditional Error Principles

Recursive Two-Stage Design

Recursive Combination Tests

Decision Function Method

Sample Size REestimation design

Opportunity

Adaptation Rules

SAS Macros for Sample Size Reesimation

Comparison of Sample Size Reesimation Methods

Analysis of Design with Sample Size Adjustment

Trial Example: Prevention of Myocardial Infarction

Multiple-Endpoint Adaptive design

Multiplicity Issues

Multiple-Endpoint Adaptive Design

Drop-Loser and Add-Arm Designs

Opportunity

Method with Week Alpha-Control

Method with Strong Alpha-Control

Application of SAS Macro for Drop-Loser Design

Biomarker-Adaptive Design

Opportunities

Design with Classifier Biomarker

Challenges in Biomarker Validation

Adaptive Design with Prognostic Biomarker

Adaptive Design with Predictive Marker

Adaptive Treatment Switching and Crossover

Treatment Switching and Crossover

Mixed Exponential Survival Model

Threshold Regression

Latent Event Time Model for Treatment Crossover

Response-Adaptive Allocation Design

Opportunities

Adaptive Design with RPW

General Response-Adaptive Randomization (RAR)

Adaptive Dose Finding design

Oncology Dose-Escalation Trial

Continual Reassessment Method (CRM)

Bayesian Adaptive Design

Introduction

Bayesian Learning Mechanism

Bayesian Basics

Trial Design

Trial Monitoring

Analysis of Data

Interpretation of Outcomes

Regulatory Perspective

Planning, Execution, Analysis, and Reporting

Validity and Integrity

Study Planning

Working with Regulatory Agency

Trial Monitoring

Analysis and Reporting

Bayesian Approach

Clinical Trial Simulation

Paradox—Debates in Adaptive Designs

My Standing Point

Decision Theory Basics

Evidence Measure

Statistical Principles

Behaviors of Statistical Principles in Adaptive Designs

Appendix A: Random Number Generation

Random Number

Uniformly Distributed Random Number

Inverse CDF Method

Acceptance-Rejection Methods

Multivariate Distribution

Appendix B: Implementing Adaptive Designs in R

Bibliography

INDEX

Summaries and Research Problems/Exercises appear at the end of each chapter.