KyungMann Kim is Professor of Biostatistics and Statistics and Director of Clinical Trials Program, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison. He is a former associate editor of Biometrics and an elected Fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for Advancement of Science.Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He is also an Adjunct Professor at the Hannover Medical School (Germany) and the Medical University Vienna (Austria). He is a former editor-in-chief of Statistics in Biopharmaceutical Research. a Fellow of the American Statistical Association, and a recipient of the Susanne-Dahms-Medal from the German Region of the International Biometric Society.Ying Kuen (Ken) Cheung is Professor of Biostatistics and Associate Dean for Faculty in the Mailman School of Public Health at Columbia University. He is a recipient of the IBM Faculty Award on Big Data and Analytics. He is a Fellow of the American Statistical Association and a Fellow of the New York Academy of Medicine.Lisa Hampson is a Director in Statistical Methodology at Novartis.

"This book is the product of a large and outstanding group of editors and collaborative authors who undertook a huge effort of summarizing, in one volume, a subject spanning a wide crosssection of topics related to clinical trials. ... Throughout, many topics are illustrated with examples of recently reported trials adding to the applicability of the corresponding theory. The emphasis on sample size estimation is a very nice touch and a strong feature of the book. In some cases, authors have included code in R and SAS to assist users."-Daniel Zelterman in Technometrics, July 2022"This book has the power to be a reference for teaching or research. This book provides a comprehensive guide to statistical methods relevant to randomized, controlled clinical trials. The authors explain each chapter in detail, providing an in-depth understanding of the concepts and techniques involved. The authors in this book are leading experts in their fields. The authors have extensive experience and knowledge in statistics and randomized controlled clinical trials, thereby lending authority and reliability to the content presented. A good balance between the statistical theory underlying the method and practical application in the context of randomized controlled clinical trials. Readers can understand the theoretical basics and observe the practical application of the methods to real data. This book includes examples and case studies that use real data. Readers gain a practical understanding of the methods taught in the book and an overview of their application in randomized controlled clinical trials."-Tri Astari in Technometrics, February 2024

• Part I. Introduction to Randomized, Controlled Trials. 1. Introduction. Part II. Analytic Methods for Randomized, Controlled Trials. 2. Dichotomous and ordinal: chi-square and Fisher's exact tests and binary regression models. 3. Continuous: t-test, Wilcoxon-test, and linear or non-linear regression models. 4. Time to event subject to censoring: logrank test, Kaplan-Meier estimation and Cox proportional hazards regression models. 5. Count: Poisson and negative binomial regression models. 6. Longitudinal: Linear and generalized linear mixed models, GEE. 7. Recurrent events. 8. Cross-over design. 9. Factorial design. 10. Cluster randomized design. 11. Randomization, stratification, and outcome-adaptive allocation. 12. Sample size estimation and power analysis: Dichotomous, ordinal, continuous and count. 13. Sample size estimation and power analysis: Time-to-event data subject to censoring. 14. Sample size estimation and power analysis: Longitudinal data. 15. Group sequential methods, triangular methods and stochastic curtailments. 16. Sample size re-estimation. 17. Adaptive designs. 18. Multiple testing. 19. Subgroup analysis. 20. Competing risks. 21. Joint models for longitudinal markers and clinical outcomes. 22. Sequential multiple assignment randomization trial (SMART) for dynamic treatment allocation. 23. Safety data analysis. 24. Non-inferiority trials. 25. Incorporating historical data into RCTs. 26. Validation of surrogate outcomes.

KyungMann Kim is Professor of Biostatistics and Statistics and Director of Clinical Trials Program, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison. He is a former associate editor of Biometrics and an elected Fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for Advancement of Science.Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He is also an Adjunct Professor at the Hannover Medical School (Germany) and the Medical University Vienna (Austria). He is a former editor-in-chief of Statistics in Biopharmaceutical Research. a Fellow of the American Statistical Association, and a recipient of the Susanne-Dahms-Medal from the German Region of the International Biometric Society.Ying Kuen (Ken) Cheung is Professor of Biostatistics and Associate Dean for Faculty in the Mailman School of Public Health at Columbia University. He is a recipient of the IBM Faculty Award on Big Data and Analytics. He is a Fellow of the American Statistical Association and a Fellow of the New York Academy of Medicine.Lisa Hampson is a Director in Statistical Methodology at Novartis.

"This book is the product of a large and outstanding group of editors and collaborative authors who undertook a huge effort of summarizing, in one volume, a subject spanning a wide crosssection of topics related to clinical trials. ... Throughout, many topics are illustrated with examples of recently reported trials adding to the applicability of the corresponding theory. The emphasis on sample size estimation is a very nice touch and a strong feature of the book. In some cases, authors have included code in R and SAS to assist users."-Daniel Zelterman in Technometrics, July 2022"This book has the power to be a reference for teaching or research. This book provides a comprehensive guide to statistical methods relevant to randomized, controlled clinical trials. The authors explain each chapter in detail, providing an in-depth understanding of the concepts and techniques involved. The authors in this book are leading experts in their fields. The authors have extensive experience and knowledge in statistics and randomized controlled clinical trials, thereby lending authority and reliability to the content presented. A good balance between the statistical theory underlying the method and practical application in the context of randomized controlled clinical trials. Readers can understand the theoretical basics and observe the practical application of the methods to real data. This book includes examples and case studies that use real data. Readers gain a practical understanding of the methods taught in the book and an overview of their application in randomized controlled clinical trials."-Tri Astari in Technometrics, February 2024

• Part I. Introduction to Randomized, Controlled Trials. 1. Introduction. Part II. Analytic Methods for Randomized, Controlled Trials. 2. Dichotomous and ordinal: chi-square and Fisher's exact tests and binary regression models. 3. Continuous: t-test, Wilcoxon-test, and linear or non-linear regression models. 4. Time to event subject to censoring: logrank test, Kaplan-Meier estimation and Cox proportional hazards regression models. 5. Count: Poisson and negative binomial regression models. 6. Longitudinal: Linear and generalized linear mixed models, GEE. 7. Recurrent events. 8. Cross-over design. 9. Factorial design. 10. Cluster randomized design. 11. Randomization, stratification, and outcome-adaptive allocation. 12. Sample size estimation and power analysis: Dichotomous, ordinal, continuous and count. 13. Sample size estimation and power analysis: Time-to-event data subject to censoring. 14. Sample size estimation and power analysis: Longitudinal data. 15. Group sequential methods, triangular methods and stochastic curtailments. 16. Sample size re-estimation. 17. Adaptive designs. 18. Multiple testing. 19. Subgroup analysis. 20. Competing risks. 21. Joint models for longitudinal markers and clinical outcomes. 22. Sequential multiple assignment randomization trial (SMART) for dynamic treatment allocation. 23. Safety data analysis. 24. Non-inferiority trials. 25. Incorporating historical data into RCTs. 26. Validation of surrogate outcomes.