Methods for the Analysis of Multiple Endpoints in Small Populations a Review

Due to the schedule constraint of the instructors, the morning part of the curt form (C2A) will start at 10am and stop at 12pm, and C2B will outset at 1:15pm and end at 4:15pm.

C2A/C2B

Chair: Yeh-Fong Chen (FDA)
Instructors:
Toshimitsu Hamasaki (GWU)
James Hung (FDA)

In confirmatory clinical trials, as oft is the case, a single clinical issue is selected as a primary endpoint. This endpoint serves as the ground for the trial blueprint including sample size determination, interim data monitoring, last analyses and the reporting of the trial results. The primary endpoint is an outcome providing the most clinically relevant measure for the primary objective of a trial. All the same, a single primary endpoint often cannot provide comprehensive characterization about the of import effects of the intervention as the diseases are caused by interdependent multiple factors such as genetic, environmental, lifestyle and other factors. For this reason, clinical trials may yet be designed with either a single principal endpoint plus cardinal secondary endpoints or more than one primary endpoint. Assessing multiple endpoints may offer an bonny blueprint feature as they could capture multiple characteristics of the effects of an intervention and provide more informative intervention comparisons. Nevertheless multiple endpoints besides create challenges in design, interim monitoring, analysis and reporting of clinical trials. This brusque course will be devoted to give-and-take of some challenging statistical issues with multiple endpoints and statistical methods to meet the challenges. The outline of the short course includes:

• provide an overview of the concepts, principle and procedures for testing multiple endpoints
• discuss challenging problems with analysis of a single chief endpoint plus key secondary endpoints and more i principal endpoint.
• review relevant regulatory guidelines and related documents
• integrate contempo methodological developments for pattern, monitoring and assay of clinical trials with multiple endpoints
• include case studies from actual clinical trials to assist attendees quickly empathize common methods and apply them to problems in real world.
• demonstrate how to implement the methods using statistical software, e.g., SAS and R

Instructors:
Toshimitsu Hamasaki, PhD, MS, pstat®
Research Professor
The George Washington University Biostatistics Center

Toshimitsu Hamasaki is a Research Professor of the George Washington University (GWU) Biostatistics Center and the Department of Biostatistics and Bioinformatics. He has been involved in biopharmaceutical statistics and clinical trials for over 25 years. Prior to joining GWU, he worked at Shiogoni, Pfizer, Osaka University and National Cognitive and Cardiovascular Center. His research interests include the pattern, monitoring, analyses, and reporting of clinical trials. He is the author of more than 200 peer-reviewed publications and four textbooks on statistical methods in clinical trials.

Dr. Hamasaki received the Ph.D. degree in Engineering science from Osaka University in 1998. He was an Associate Editor for Statistics in Biopharmaceutical Research, Journal of Biopharmaceutical Statistics and Japanese Periodical of Statistics and Data Science, and Editor for CHANCE, and currently serves as Editor-in-Main of Statistics in Biopharmaceutical Enquiry. He is a fellow member of the Steering Commission for the Adaptive designs CONSORT Extension (ACE) Projection, an extension to the CONsolidated Standards of Reporting Trials (CONSORT) Statement for adaptive clinical trials. Dr. Hamasaki is an elected member of International Statistical Institute and a fellow of the American Statistical Clan. He received the Distinguished Commodity Award from the Japanese Society of Computational Statistics in 1997 and Hida-Mizuno Prize from the Behaviormetric Society of Japan in 2003.

H.G. James Hung, PhD
Director of Division of Biometrics I
Office of Biostatistics, Function of Translational Sciences
Center for Drug Evaluation and Research
U.Southward. Food and Drug Administration

Dr. H.M. James Hung is Director of Division of Biometrics I (DBI), Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Enquiry (CDER), U.S. Food and Drug Assistants (FDA). The Division provides statistical services for Division of Neurology Products, Division of Psychiatry Products, Division of Anesthesia, Analgesia, Addiction Products, and Sectionalization of Medical Imaging Products, CDER.

During his tenure with FDA, he had reviewed many large bloodshed/morbidity clinical trials in cardiovascular and renal disease areas and provided guidance to DBI review teams in reviewing the awarding of adaptive designs and innovative designs to registration clinical trials. He published many peer reviewed articles in Biometrics, Statistics in Medicine, Controlled Clinical Trials, Biometrical Periodical, Journal of Biopharmaceutical Statistics, and Pharmaceutical Statistics. His research areas include multi-regional clinical trials, adaptive design/analysis, non-inferiority trials, factorial design clinical trials, utility of p-value distribution, and some of the advanced methodologies in central nervous organisation trials. He delivered more than 200 invited talks, lectures or brusque courses in many countries and published more than fourscore manufactures.

Dr. Hung received two FDA/CDER Scientific Achievement Awards and many other awards for the recognition of his scientific contributions to the US FDA. He is the recipient of 2011 FDA Scientific Achievement Award – Excellence of Analytic Science. He also received awards for recognizing his contributions to the FDA guidance documents on adaptive designs and non-inferiority trial designs.

Dr. Hung is a Fellow of the American Statistical Association and an elected fellow member of the International Statistical Establish. He served as an Editor-in-Primary for Journal of Pharmaceutical Statistics in 2009-2011. Currently, he serves as an Associate Editor for Statistics in Medicine.

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Source: https://sites.duke.edu/diss/short-courses/design-data-monitoring-and-analysis-of-clinical-trials-with-multiple-outcomes/

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