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Introductory Adaptive Trial Designs

A Practical Guide with R

Mark Chang



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Productivity Press
08 June 2015
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.
By:   Mark Chang
Imprint:   Productivity Press
Country of Publication:   United States
Volume:   75
Dimensions:   Height: 234mm,  Width: 156mm,  Spine: 15mm
Weight:   544g
ISBN:   9781498717465
ISBN 10:   1498717462
Series:   Chapman & Hall/CRC Biostatistics Series
Pages:   232
Publication Date:   08 June 2015
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
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.

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