Description
Book SynopsisStatistical 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 questi
Trade Review
"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
Table of ContentsPart 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.
