Adaptive Design Methods in Clinical Trials (Chapman & Hall/CRC Biostatistics Series 2nd Revised edition)
By: Shein-Chung Chow (author), Mark Chang (author)Hardback
2 - 4 weeks availability
With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA's recent draft guidance, a new edition of one of the first books on the topic is needed.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 Edition Along 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.
Introduction What Is Adaptive Design Regulatory Perspectives Target Patient Population Statistical Inference Practical Issues Aims and Scope of the Book Protocol Amendment Introduction Moving Target Patient Population Analysis with Covariate Adjustment Assessment of Sensitivity Index Sample Size Adjustment Concluding Remarks Adaptive Randomization Conventional Randomization Treatment-Adaptive Randomization Covariate-Adaptive Randomization Response-Adaptive Randomization Issues with Adaptive Randomization Summary Adaptive Hypotheses Modifications of Hypotheses Switch from Superiority to Noninferiority Concluding Remarks Adaptive Dose-Escalation Trials Introduction CRM in Phase I Oncology Study Hybrid Frequentist-Bayesian Adaptive Design Design Selection and Sample Size Concluding Remarks Adaptive Group Sequential Design Sequential Methods General Approach for Group Sequential Design Early Stopping Boundaries Alpha Spending Function Group Sequential Design Based on Independent P-Values Calculation of Stopping Boundaries Group Sequential Trial Monitoring Conditional Power Practical Issues Statistical Tests for Seamless Adaptive Designs Why a Seamless Design Is Efficient Step-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 Data Cui-Hung-Wang's Method Proschan-Hunsberger's Method Muller-Schafer Method Bauer-Kohne Method Generalization of Independent P-Value Approaches Inverse-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 Switching Latent Event Times Proportional Hazard Model with Latent Hazard Rate Mixed Exponential Model Concluding Remarks Bayesian Approach Basic Concepts of Bayesian Approach Multiple-Stage Design for Single-Arm Trial Bayesian 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 Simulation Introduction Software Application of ExpDesign Studio Early Phases Development Late Phases Development Concluding 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 Studies Basic Considerations Adaptive Group Sequential Design Adaptive Dose-Escalation Design Two-Stage Phase II/III Adaptive Design Bibliography Subject Index
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- ID: 9781439839874
2nd Revised edition
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