Handbook of Statistical Methods for Randomized Controlled Trials  book cover
1st Edition

Handbook of Statistical Methods for Randomized Controlled Trials

ISBN 9781498714624
Published August 23, 2021 by Chapman and Hall/CRC
654 Pages 64 B/W Illustrations

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Book Description

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.

Table of Contents

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



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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.