Clinical Trial Optimization Using R

Alex Dmitrienko, Erik Pulkstenis

June 7, 2017 by Chapman and Hall/CRC
Reference - 319 Pages - 100 B/W Illustrations
ISBN 9781498735070 - CAT# K26410
Series: Chapman & Hall/CRC Biostatistics Series


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will provide a lot of tutorial material to help biostatisticians and statistical analysts who are new to a certain area of clinical trials quickly understand the key approaches/challenges, determine ways to finding optimal solutions and apply them to real-life problems.

will offer valuable advice from subject-matter experts and discussion of relevant regulatory considerations.

will feature a very large number of case studies to make it more appealing to a broad audience of biopharmaceutical researchers.

will emphasize a flexible, simulation-based approach to tackling complex optimization problems related to sample size determination and power evaluation in clinical trials.

will be structured around an R package (Mediana package) and supplementary material, including the code, will be available on the web site


Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making.

This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.