Completely revised and updated, the second edition of this classic text retains its predecessor's careful balance of theory and practice while incorporating new approaches, more data sets, and a broader scope. Enhancements in this edition include a new chapter on bioequivalence, methods for analyzing longitudinal continuous and categorical data, extensive real-life examples, and SAS code that allows readers to implement the analyses described. With downloadable SAS programs, thorough treatment of design issues, and a comprehensive catalog of designs, this edition sets a new standard for texts in this area and will undoubtedly be of direct practical value for years to come.
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Completely revised and updated, the second edition of this classic text retains its predecessor's careful balance of theory and practice while incorporating new approaches, more data sets, and a broader scope. Enhancements in this edition include a new chapter on bioequivalence, methods for analyzing longitudinal continuous and categorical data, extensive real-life examples, and SAS code that allows readers to implement the analyses described. With downloadable SAS programs, thorough treatment of design issues, and a comprehensive catalog of designs, this edition sets a new standard for texts in this area and will undoubtedly be of direct practical value for years to come.
Pharmaceutical companies throughout the world invest enormous resources in drug development and testing. The cross-over trial is an important type of trial in which each subject acts as its own control, leading to more accurate treatment comparisons. Given the high cost of clinical trials, the effective use of resources is vital and this requires a sound appreciation of the statistical issues in the design and analysis of cross-over trials. Representing the state of the art at present in cross-over trials, this text aims to act as a basic reference text for future research as well as a source of practical and useful advice for statisticians and clinicians involved in the planning and analysis of cross-over trials.
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