A comprehensive and practical resource for analyses of crossover designs
For ethical reasons, it isvital to keep the number of patients in a clinical trial aslow as possible.As evidenced by extensive research publications, crossover designcan bea useful and powerfultool to reduce the number of patients needed for a parallel group design in studying treatmentsfor 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 valuableresource for researchers involved incrossover trial design.
- 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.
Avainsanat: Crossover design;Random Effects model;Exact test procedure;Exact interval estimator;Latin-square;Continuous data;Dichotomous data;Ordinal data;Frequency data;Sample size, Medical Statistics & Epidemiology, Epidemiology & Biostatistics, Medical Statistics & Epidemiology, Epidemiology & Biostatistics