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 variety of issues of non-inferiority trials, including multiple comparisons, missing data, analysis population, the use of safety margins, the internal consistency of non-inferiority inference, the use of surrogate endpoints, trial monitoring, and equivalence trials
- Specific issues and analysis methods when the data are binary, continuous, and time-to-event
- The history of non-inferiority trials and the design and conduct considerations for a non-inferiority trial
- The strength of evidence of an efficacy finding and how to evaluate the effect size of an active control therapy
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.
Table of Contents
What Is a Non-Inferiority Trial? Non-Inferiority Trial. Considerations. Strength of Evidence and Reproducibility. Evaluating the Active Control Effect. Across-Trials Analysis Methods. Three-Arm Non-Inferiority Trials. Multiple Comparisons. Missing Data and Analysis Sets. Safety Studies. Additional Topics. Inference on Proportions. Inferences on Means and Medians. Inference on Time-to-Event End Points. Appendix: Statistical Concepts. Index.
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.