A unique overview that melds the concepts of conditional probability and stochastic processes into real-life applicationsThe role of randomization techniques in clinical trials has become increasingly important. This comprehensive guide combines both the applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization. Taking an unabashedly non-Bayesian and nonparametric approach to inference, the book focuses on the linear rank test under a randomization model, with added discussion on likelihood-based inference as it relates to sufficiency and ancillarity. Developments in stochastic processes and applied probability are also given where appropriate. Intuition is stressed over mathematics, but not without a clear development of the latter in the context of the former.Providing a consolidated review of the field, the book includes relevant and practical discussions of:* The benefits of randomization in terms of reduction of bias* Randomization as a basis for inference* Covariate-adaptive and response-adaptive randomization* Current philosophies, controversies, and new developmentsWith ample problem sets, theoretical exercises, and short computer simulations using SAS, Randomization in Clinical Trials: Theory and Practice is equally useful as a standard textbook in biostatistics graduate programs as well as a reliable reference for biostatisticians in practice.