Jay Bartroff: Sequential Experimentation in Clinical Trials, Gebunden

Kurzbeschreibung

This book examines sequential experimentation, including group sequential and adaptive designs of Phase II and III clinical trials. It includes recent work that provides a new class of adaptive designs which are both flexible and efficient.

Inhaltsangabe

Introduction.- Nonlinear Regression, Experimental Design, and Phase I Clinical Trials.- Sequential Testing Theory and Stochastic Optimization over Time.- Group Sequential Design of Phase II and III Trials.- Sequential Methods for Vaccine Safety Evaluation and Surveillance in Public Health.- Time-Sequential Design of Clinical Trials with Failure-Time Endpoints.- Confidence Intervals and p-Values.- Adaptive Design of Confirmatory Trials.- References.

Rezension

From the book reviews:
It is definitely a must read for anyone doing research into the theory or methodology of modern sequential statistical analysis. (Bruce W. Turnbull, Mathematical Reviews, May, 2014)

Klappentext

Sequential Experimentation in Clinical Trials: Design and Analysis is developed from decades of work in research groups, statistical pedagogy, and workshop participation. Different parts of the book can be used for short courses on clinical trials, translational medical research, and sequential experimentation. The authors have successfully used the book to teach innovative clinical trial designs and statistical methods for Statistics Ph. D. students at Stanford University. There are additional online supplements for the book that include chapter-specific exercises and information.
Sequential Experimentation in Clinical Trials: Design and Analysis covers the much broader subject of sequential experimentation that includes group sequential and adaptive designs of Phase II and III clinical trials, which have attracted much attention in the past three decades. In particular, the broad scope of design and analysis problems in sequential experimentation clearly requires a wide range of statistical methods and models from nonlinear regression analysis, experimental design, dynamic programming, survival analysis, resampling, and likelihood and Bayesian inference. The background material in these building blocks is summarized in Chapter 2 and Chapter 3 and certain sections in Chapter 6 and Chapter 7. Besides group sequential tests and adaptive designs, the book also introduces sequential change-point detection methods in Chapter 5 in connection with pharmacovigilance and public health surveillance. Together with dynamic programming and approximate dynamic programming in Chapter 3, the book therefore covers all basic topics for a graduate course in sequential analysis designs.