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.
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