Adaptive Design Theory and Implementation Using Sas and R

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.

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.