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A comprehensive and practical resource for analyses of crossover designs For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible. As evidenced by extensive research publications, crossover design can be a useful and powerful tool to reduce the number of patients needed for a parallel group design in studying treatments for non-curable chronic diseases. This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. Systematic discussion on sample size determination is also included, which will be a valuable resource for researchers involved in crossover trial design. Key features: Provides exact test procedures and interval estimators, which are especially of use in small-sample cases. Presents most test procedures and interval estimators in closed-forms, enabling readers to calculate them by use of a pocket calculator or commonly-used statistical packages. Each chapter is self-contained, allowing the book to be used a reference resource. Uses real-life examples to illustrate the practical use of test procedures and estimators Provides extensive exercises to help readers appreciate the underlying theory, learn other relevant test procedures and understand how to calculate the required sample size. Crossover Designs: Testing, Estimation and Sample Size will be a useful resource for researchers from biostatistics, as well as pharmaceutical and clinical sciences. It can also be used as a textbook or reference for graduate students studying clinical experiments.

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It is quite common in a randomized clinical trial (RCT) to encounter patients who do not comply with their assigned treatment. Since noncompliance often occurs non-randomly, the commonly-used approaches, including both the as-treated (AT) and as-protocol (AP) analysis, and the intent-to-treat (ITT) (or as-randomized) analysis, are all well known to possibly produce a biased inference of the treatment efficacy. This book provides a systematic and organized approach to analyzing data for RCTs with noncompliance under the most frequently-encountered situations. These include parallel sampling, stratified sampling, cluster sampling, parallel sampling with subsequent missing outcomes, and a series of dependent Bernoulli sampling for repeated measurements. The author provides a comprehensive approach by using contingency tables to illustrate the latent probability structure of observed data. Using real-life examples, computer-simulated data and exercises in each chapter, the book illustrates the underlying theory in an accessible, and easy to understand way. Key features: Consort-flow diagrams and numerical examples are used to illustrate the bias of commonly used approaches, such as, AT analysis, AP analysis and ITT analysis for a RCT with noncompliance. Real-life examples are used throughout the book to explain the practical usefulness of test procedures and estimators. Each chapter is self-contained, allowing the book to be used as a reference source. Includes SAS programs which can be easily modified in calculating the required sample size. Biostatisticians, clinicians, researchers and data analysts working in pharmaceutical industries will benefit from this book. This text can also be used as supplemental material for a course focusing on clinical statistics or experimental trials in epidemiology, psychology and sociology.

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