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19 black & white illustrations, 55 black & white tables
Table Of Contents
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
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.