Design and Analysis of Non-Inferiority Trials
Chapman and Hall/CRC – 2011 – 454 pages
The increased use of non-inferiority analysis has been accompanied by a proliferation of research on the design and analysis of non-inferiority studies. Using examples from real clinical trials, Design and Analysis of Non-Inferiority Trials brings together this body of research and confronts the issues involved in the design of a non-inferiority trial. Each chapter begins with a non-technical introduction, making the text easily understood by those without prior knowledge of this type of trial.
Topics covered include:
A comprehensive discussion on the purpose and issues involved with non-inferiority trials, Design and Analysis of Non-inferiority Trials will assist current and future scientists and statisticians on the optimal design of non-inferiority trials and in assessing the quality of non-inferiority comparisons done in practice.
It is a pleasure to see a book completely devoted to the challenging arena of non-inferiority trials. … I am very impressed with its depth and breadth, and believe that it will be an important resource for anyone involved in designing non-inferiority trials. The authors weave in many examples, primarily in oncology, as well as a large set of references from the now substantial statistical literature on non-inferiority designs. … This book is a must-have resource for those involved in non-inferiority trials for the pharmaceutical industry, and a must-read for those new to non-inferiority trials. A portion of a special topics course in a biostatistics department could be built around this book, and this exposure would be especially valuable for students considering a career in or around the pharmaceutical industry.
—Erica Brittain, Australian & New Zealand Journal of Statistics, May 2012
This is the first book which is devoted solely to non-inferiority studies. All three authors have published several papers on that topic over the last years. This comprehensive book covers in more than 400 pages nearly all aspects about non-inferiority trials, and beyond. It is also an excellent source of references about non-inferiority studies. … recommended for anyone working with clinical trials and in particular for those working in late phase drug development. It is an excellent source of concepts and statistical methods relevant for biostatisticians, clinical epidemiologists and students. This book also is a good source for non-inferiority studies for scientists from the clinical field.
—Steffen Witte, Journal of Biopharmaceutical Statistics, 2012
What Is a Non-Inferiority Trial?
Definition of Non-Inferiority
Reasons for Non-Inferiority Trials
Different Types of Comparisons
A History of Non-Inferiority Trials
Non-Inferiority Trial Considerations
External Validity and Assay Sensitivity
Critical Steps and Issues
Sizing a Study
Example of Anti-Infectives
Strength of Evidence and Reproducibility
Strength of Evidence
Evaluating the Active Control Effect
Active Control Effect
Across-Trials Analysis Methods
Two Confidence Interval Approaches
Comparing Analysis Methods and Type I Error Rates
A Case in Oncology
Three-Arm Non-Inferiority Trials
Comparisons to Concurrent Controls
Comparing Multiple Groups to an Active Control
Non-Inferiority on Multiple End Points
Testing for Both Superiority and Non-Inferiority
Missing Data and Analysis Sets
Considerations for Safety Study
Cardiovascular Risk in Antidiabetic Therapy
Surrogate End Points
Inference on Proportions
Fixed Thresholds on Differences
Fixed Thresholds on Ratios
Fixed Thresholds on Odds Ratios
Stratified and Adjusted Analyses
Inferences on Means and Medians
Fixed Thresholds on Differences of Means
Fixed Thresholds on Ratios of Means
Analyses Involving Medians
Inference on Time-to-Event End Points
Nonparametric Inference Based on a Hazard Ratio
Analyses Based on Landmarks and Medians
Comparisons Over Preset Intervals
Appendix: Statistical Concepts
Comparison of Methods
Stratified and Adjusted Analyses
Dr. Mark Rothmann earned his Ph. D. in Statistics at the University of Iowa. He taught several years as a professor and has worked at the U. S. Food and Drug Administration. He has done research in many areas involving the design and analysis of clinical trials.
Dr. Brian L. Wiens, received his MS in statistics from Colorado State University and his Ph.D. in statistics from Temple University. He has worked at several pharmaceutical, biotechnology and medical device companies since 1991. He has published research in several areas of design and analysis of clinical trials. Dr. Wiens is a Fellow of the American Statistical Association.
Dr. Ivan S.F. Chan received his M.S. in Statistics from The Chinese University of Hong Kong and his Ph.D. in Biostatistics from University of Minnesota. He has worked at Merck Research Laboratories since 1995 and is currently Senior Director and Franchise Head for vaccines, leading the statistical support for all vaccine clinical research programs at Merck. Dr. Chan has published research in many areas of statistics including exact inference, analysis of non-inferiority trials, and methodologies for clinical trials.