Randomization, Masking, and Allocation Concealment is indispensable for any trial researcher who wants to use state of the art randomization methods, and also wants to be able to describe these methods correctly.
Far too often the subtle nuances that distinguish proper randomization from flawed randomization are completely ignored in trial reports that state only that randomization was used, with no additional information. Experience has shown that in many cases, the type of randomization that was used was flawed. It is only a matter of time before medical journals and regulatory agencies come to realize that we can no longer rely on (or publish) flawed trials, and that flawed randomization in and of itself disqualifies a trial from being robust or high quality, even if that trial is of high quality otherwise.
This book will help to clarify the role randomization plays in ensuring internal validity, and in drawing valid inferences from the data. The various chapters cover a variety of randomization methods, and are not limited to the most common (and most flawed) ones. Readers will come away with a profound understanding of what constitutes a valid randomization procedure, so that they can distinguish the valid from the flawed among not only existing methods but also methods yet to be developed.
Table of Contents
Randomization and Bias in Historical Perspective
J. Rosser Matthews
Proper Randomization Reduces the Chance of Waste Biomedical Research
Sympathetic bias: a Neglected Source of Selection Bias
William C. Grant
The Alleged Benefits of Unrestricted Randomization
Vance W. Berger
Restricted Randomization: Pros and Cautions
Evolution of Restricted Randomization with Maximum Tolerated Imbalance
Evaluating the Evaluation
Adriana C. Burgos, Ross J. Kusmick
Selection Bias in Studies with Unequal Allocation
Olga M. Kuznetsova
Unrecognized Dual Threats to Internal Validity Relating to Randomization
Vance W. Berger, Adriana C. Burgos, Omolola A. Odejimi
Testing for second-order selection bias effect in randomised control trials using reverse propensity score (RPS)
Steffen Mickenautsch, Bo Fu
The Berger-Exner Test to Detect Third Order Selection Bias in the Presence of a True Treatment Effect
Steffen Mickenautsch, Bo Fu, Vance W. Berger
Adjusting for and detection of selection bias in randomized controlled clinical trials
Lieven N. Kennes
Randomization and the randomization test: Two sides of the same coin
Randomization tests or permutation tests? A historical and terminological clarification
Flexible Minimization: Synergistic Solution for Selection Bias
Donald R. Taves
Vance W. Berger received holds a doctoral degree in Statistics from Rutgers University. His professional career has included work in the pharmaceutical industry (Janssen Research Foundation, Theradex, and some consulting for Pfizer), work in two centers of the Food and Drug Administration (Drugs and Biologics), and review work for a number of statistical and medical journals. An active researcher, Dr. Berger wrote a book on the design and analysis of randomized clinical trials (focusing on randomization methods). He has also authored numerous book chapters and scientific articles appearing in the peer-reviewed literature, and has presented numerous invited lectures on this topic. Dr. Berger was the recipient of the 2006 Gertrude Cox Award, recognizing "a statistician making significant contributions to statistical practice" by the Washington Statistical Society.