1st Edition

Handbook of Statistical Methods for Randomized Controlled Trials

    654 Pages 64 B/W Illustrations
    by Chapman & Hall

    654 Pages 64 B/W Illustrations
    by Chapman & Hall

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    Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials.

    Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

    Part I. Introduction to Randomized, Controlled Trials

    1. Introduction
    KyungMann Kim

    Part II. Analytic Methods for Randomized, Controlled Trials

    2. Dichotomous and ordinal: chi-square and Fisher's exact tests and binary regression models
    Garrett Fitzmaurice, Stuart Lipsitz

    3. Continuous: t-test, Wilcoxon-test, and linear or non-linear regression models
    Fang-Shu Ou

    4. Time to event subject to censoring: logrank test, Kaplan-Meier estimation and Cox proportional hazards regression models
    Daniel Scharfstein, Yuxin Zhu, Anastasios Tsiatis

    5. Count: Poisson and negative binomial regression models
    Jianguo "Tony" Sun, Xin He

    6. Longitudinal: Linear and generalized linear mixed models, GEE
    Myunghee Cho Paik, Soeun Kim

    7. Recurrent events
    Richard Cook, Yujie Zhong

    8. Cross-over design
    Stephen Senn

    9. Factorial design
    Bibhas Chakraborty, Palash Ghosh

    10. Cluster randomized design
    Martin Bland, Mona Kanaan

    11. Randomization, stratification, and outcome-adaptive allocation
    Oleksandr Sverdlov, Yevgen Ryeznik

    12. Sample size estimation and power analysis: Dichotomous, ordinal, continuous and count
    Keaven Anderson, Oliver Bautista

    13. Sample size estimation and power analysis: Time-to-event data subject to censoring
    Keaven Anderson, Oliver Bautista

    14. Sample size estimation and power analysis: Longitudinal data
    Sin-Ho Jung

    15. Group sequential methods, triangular methods and stochastic curtailments
    Michael Proschan

    16. Sample size re-estimation
    Tim Friede, Tobias Mütze

    17. Adaptive designs
    Gernot Wassmer, Franz Koenig, Martin Posch

    18. Multiple testing
    Jason Hsu, Yi Liu, Szu-Yu Tang

    19. Subgroup analysis
    Rui Wang

    20. Competing risks
    Haesook Kim

    21. Joint models for longitudinal markers and clinical outcomes
    Helene Jacqmin-Gadda, Cécile Proust-Lima, Loïc Ferrer

    22. Sequential multiple assignment randomization trial (SMART) for dynamic treatment allocation
    Michael Kosorok, Emily Butler

    23. Safety data analysis
    Amy Xia, Brenda Crowe, Jesse Berlin

    24. Non-inferiority trials
    Brian Wiens

    25. Incorporating historical data into RCTs
    Heinz Schmidli, Sandro Gsteiger, Beat Neuenschwander

    26. Validation of surrogate outcomes
    Geert Molenberghs


     

     

    Biography

    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