Adaptive Design Methods in Clinical Trials

With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA’s recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clinical trials. It unifies the vast and continuously growing literature and research activities on regulatory requirements, scientific and practical issues, and statistical methodology.
New to the Second EditionAlong with revisions throughout the text, this edition significantly updates the chapters on protocol amendment and clinical trial simulation to incorporate the latest changes. It also includes five entirely new chapters on two-stage adaptive design, biomarker adaptive trials, target clinical trials, sample size and power estimation, and regulatory perspectives.

Following in the tradition of its acclaimed predecessor, this second edition continues to offer an up-to-date resource for clinical scientists and researchers in academia, regulatory agencies, and the pharmaceutical industry. Written in an intuitive style at a basic mathematical and statistical level, the book maintains its practical approach with an emphasis on concepts via numerous examples and illustrations.

Shein-Chung Chow is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also an adjunct professor of clinical sciences at Duke–National University of Singapore Graduate Medical School and the editor-in-chief of the Journal of Biopharmaceutical Statistics. He has authored or co-authored numerous papers and books, including the Handbook of Adaptive Designs in Pharmaceutical and Clinical Development and Controversial Statistical Issues in Clinical Trials.
Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals and an adjunct professor at Boston University. A fellow of the American Statistical Association, Dr. Chang is a co-founder of the International Society for Biopharmaceutical Statistics and serves on the editorial boards of two statistical journals. He has authored many publications, including Adaptive Design Theory and Implementation Using SAS and R and Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies.

IntroductionWhat Is Adaptive Design Regulatory Perspectives Target Patient Population Statistical Inference Practical IssuesAims and Scope of the Book

Protocol AmendmentIntroductionMoving Target Patient Population Analysis with Covariate AdjustmentAssessment of Sensitivity IndexSample Size AdjustmentConcluding Remarks

Adaptive RandomizationConventional Randomization Treatment-Adaptive Randomization Covariate-Adaptive Randomization Response-Adaptive Randomization Issues with Adaptive Randomization Summary

Adaptive HypothesesModifications of Hypotheses Switch from Superiority to Noninferiority Concluding Remarks

Adaptive Dose-Escalation TrialsIntroduction CRM in Phase I Oncology StudyHybrid Frequentist-Bayesian Adaptive DesignDesign Selection and Sample SizeConcluding Remarks

Adaptive Group Sequential Design Sequential Methods General Approach for Group Sequential DesignEarly Stopping Boundaries Alpha Spending FunctionGroup Sequential Design Based on Independent P-Values Calculation of Stopping Boundaries Group Sequential Trial Monitoring Conditional Power Practical Issues

Statistical Tests for Seamless Adaptive DesignsWhy a Seamless Design Is EfficientStep-Wise Test and Adaptive Procedures Contrast Test and Naive P-Value Comparisons of Seamless Design Drop-the-Loser Adaptive Design Summary

Adaptive Sample Size Adjustment Sample Size Re-Estimation without Unblinding DataCui-Hung-Wang’s MethodProschan-Hunsberger’s Method Müller-Schafer Method Bauer-Köhne MethodGeneralization of Independent P-Value ApproachesInverse-Normal Method Concluding Remarks

Two-Stage Adaptive Design Introduction Practical Issues Types of Two-Stage Adaptive Designs Analysis for Seamless Design with Same Study Objectives/Endpoints Analysis for Seamless Design with Different Endpoints Analysis for Seamless Design with Different Objectives/Endpoints Concluding Remarks

Adaptive Treatment SwitchingLatent Event Times Proportional Hazard Model with Latent Hazard RateMixed Exponential ModelConcluding Remarks

Bayesian Approach Basic Concepts of Bayesian ApproachMultiple-Stage Design for Single-Arm TrialBayesian Optimal Adaptive Designs Concluding Remarks

Biomarker Adaptive Trials Introduction Types of Biomarkers and Validation Design with Classifier Biomarker Adaptive Design with Prognostic Biomarker Adaptive Design with Predictive Marker Concluding Remarks Appendix

Target Clinical Trials Introduction Potential Impact and Significance Evaluation of Treatment Effect Other Study Designs and Models Concluding Remarks

Sample Size and Power Estimation Framework and Model/Parameter Assumptions Method Based on the Sum of P-Values Method Based on Product of P-Values Method with Inverse-Normal P-Values Sample Size Re-Estimation Summary

Clinical Trial SimulationIntroductionSoftware Application of ExpDesign StudioEarly Phases DevelopmentLate Phases DevelopmentConcluding Remarks

Regulatory Perspectives — A Review of FDA Draft Guidance Introduction The FDA Draft Guidance Well-Understood Designs Less Well-Understood Designs Adaptive Design Implementation Concluding Remarks

Case StudiesBasic ConsiderationsAdaptive Group Sequential Design Adaptive Dose-Escalation DesignTwo-Stage Phase II/III Adaptive Design

Bibliography
Subject Index