As part of ENAR's education initiative, our webinars promote continuing education for professional and student statisticians by disseminating cutting-edge knowledge to our membership. An ENAR webinar (or "WebENAR") can strengthen your background in methodology and software, provide an opportunity to learn about a topic outside of your primary area of specialization, or deepen your understanding of an area in which you already work. We invite you to participate and benefit from the expertise of some of North America's leading statisticians and biostatisticians.
The Webinar Committee of the ENAR Regional Advisory Board (RAB) is coordinating this ongoing series of 1- to 2-hour webinars given by renowned experts. Registration is free for current ENAR members. The webinars are planned to be broadly available and we encourage groups at your institution or workplace to participate together. WebENARs provide excellent learning opportunities for students and professionals alike.
Registration fees are waived for ENAR members, however, advance registration is still required for all attendees. Please email enar@enar.org if you have any questions.
Short Course | Futility Monitoring in Clinical Trials
Tuesday, July 8, 2025
12-4 pm EDT
*Special online edition of SC7 from ENAR 2025 Spring Meeting. WebENAR fees do not apply to this event. Please see Short Course fees below.*
Short Course Fees:
Member: $290 | Non-Member: $370 | Student: $185
Instructors:
Ana M. Ortega-Villa, National Institute of Allergy and Infectious Diseases
Michael Proschan, National Institute of Allergy and Infectious Diseases
Guest Speakers:
Megan Grieco, National Institute of Allergy and Infectious Diseases
Kevin Rubenstein, Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research
Jing Wang, Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research
Course Description:
At the beginning of a phase III clinical trial, there is great optimism. After all, the phase II trial results were encouraging. Then early data from the phase III trial trend the wrong way, but there is still the opportunity for the trend to reverse and become statistically significant at the end. At what point does optimism become denial of reality? How do we decide when a clinical trial is futile? What does futility even mean? This short course reviews different concepts of, and tools for evaluating, futility.
In this short course we will differentiate between operational and treatment futility, and learn tools to answer two important questions, 1) will the final result be null? And 2) will a null result be meaningful? Specific topics include conditional and predictive power, reverse conditional power, predicted intervals, predicted interval plots, revised unconditional power, beta spending functions, and an introduction to Bayesian futility monitoring.
During this short course, participants will get theoretical and hands-on practical knowledge to conduct futility analyses, interpret results, and make informed decisions. We will review case studies to illustrate the application of futility monitoring techniques in real world clinical trials. Prior to attending the short course, participants should have a general understanding of design and conduct of clinical trials, basic statistical concepts and distributions, and basic knowledge of R.
Bring your laptop (with R and RStudio already installed) and join us to learn, discuss, and implement futility monitoring techniques. We look forward to your participation!
Statistical/Programming Knowledge Required:
Instructor Bios:
Ana M. Ortega-Villa joined the National Institute of Allergy and Infectious Diseases (NIAID) in 2018 and serves as a mathematical statistician. Prior to joining the NIAID, Dr. Ortega-Villa obtained her Ph.D. in Statistics from Virginia Tech and completed post-doctoral fellowships at both the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Cancer Institute. Her interests include clinical trials, longitudinal data, mixed models, vaccines, immunology, research capacity building, statistics education, and initiatives that foster a culture of belonging.
Michael Proschan received his Ph.D. in Statistics from Florida State University in 1989. He has been a Mathematical Statistician in the Biostatistics Research Branch at the National Institute of Allergy and Infectious Diseases since January of 2006. Prior to coming to NIAID, he spent 16 years at the National Heart, Lung, and Blood Institute. He has co-authored three books: Statistical Monitoring of Clinical Trials: A Unified Approach, with Gordon Lan and Janet Wittes (Springer, 2006); Essentials of Probability Theory for Statisticians, with Pamela Shaw (CRC Press, 2016), and Statistical Thinking in Clinical Trials (CRC Press, 2022) and is a Fellow of the American Statistical Association. Dr. Proschan is also an Adjunct Professor for the Advanced Academic Programs at Johns Hopkins University.
Guest Speaker Bios:
Megan Grieco graduated from the University of Virginia in 2021 with a BS in Systems and Information Engineering, and from the Rollins School of Public Health at Emory University in 2024 with a MSPH in Biostatistics. Prior to starting in August 2024 as a master’s level biostatistician, she was also a part-time trainee during her graduate studies. Her projects focus on immunology in pediatric food allergies, as well as vaccine studies for influenza, malaria, and COVID-19. She particularly enjoys any project with a programming challenge!
Kevin Rubenstein has over 15 years of experience in statistics and medical research. Kevin joined the Office of Biostatistics Research at NIAID in 2021. He supports the design, monitoring, and data analyses for clinical research studies in a variety of infectious disease areas, including COVID-19, influenza, and mpox. Kevin completed his M.S. in Biostatistics at the University of Washington, and his B.S. in Biometry and Statistics at Cornell University.
Jing Wang has been a biostatistician with the Biostatistics Research Branch since late 2013. She holds an M.S. in Biology, with a concentration in immunology and molecular biology, from the University of Wisconsin-Milwaukee, and a second M.S. in Biostatistics from the George Washington University. Jing is passionate about statistical programming and exploring machine learning techniques.
Register for Short Course on 7/8/2025.
