Adaptive Designs for Sequential Treatment Allocation presents a rigorous theoretical treatment of the results and mathematical foundation of adaptive design theory. The book focuses on designing sequential randomized experiments to compare two or more treatments incorporating information accrued along the way.
The authors first introduce the terminology and statistical models most commonly used in comparative experiments. They then illustrate biased coin and urn designs that only take into account past treatment allocations as well as designs that use past data, such as sequential maximum likelihood and various types of doubly adaptive designs. The book also covers multipurpose adaptive experiments involving utilitarian choices and ethical issues. It ends with adaptive methods that include covariates in the design. The appendices present basic tools of optimal design theory and address Bayesian adaptive designs.
This book helps readers fully understand the theoretical properties behind various adaptive designs. Readers are then equipped to choose the best design for their experiment.
Publisher: Taylor & Francis Inc
Number of pages: 216
Weight: 454 g
Dimensions: 235 x 156 x 18 mm
"I fully endorse views of the authors that researchers working in the area of adaptive designs may find this book a useful reference. Further, since this book includes a fair number of examples, teachers of graduate-level courses on designs may also find this book useful."
-Sada Nand Dwivedi, International Society for Clinical Biostatistics
"... the book illustrates theoretical properties of adaptive designs so that researchers can choose the best design for the experiment, covering impressively diverse approaches. ... I find the book quite appealing in that the authors believed on the theoretical properties of the designs and mathematical foundations more than how and what of adaptive designs ... highly useful as a reference book in a graduate-level course on designs with nearly exhaustive approaches to adaptive design construction."
-Biometrics, December 2015
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