Adaptive Design Methods in Clinical Trials - Chow, Shein-Chung
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Présentation Adaptive Design Methods In Clinical Trials Format Broché
- Livre Sciences de la vie et de la terre
Résumé :
This 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. This edition significantly updates the chapters on p...
Biographie: Introduction. Protocol Amendment. Adaptive Randomization. Adaptive Hypotheses. Adaptive Dose-Escalation Trials. Adaptive Group Sequential Design. Statistical Tests for Seamless Adaptive Designs. Adaptive Sample Size Adjustment. Two-Stage Adaptive Design. Adaptive Treatment Switching. Bayesian Approach. Biomarker Adaptive Trials. Target Clinical Trials. Sample Size and Power Estimation. Clinical Trial Simulation. Regulatory Perspectives - A Review of FDA Draft Guidance. Case Studies. Bibliography. Subject Index.
Sommaire: 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.
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