ENAR Webinar Series (WebENARs)

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.

WebENAR Registration Fees

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.

Schedule of Upcoming WebENARs

From Numbers to Impact: The Role of Statistics Outreach Programs

Friday, June 7, 2024
10 AM – 12 PM

Brittney Bailey, Amherst College
David Kline, Wake Forest University School of Medicine
Kay See Tan, Memorial Sloan Kettering Cancer Center


Statistics outreach programs play a vital role in encouraging the next generation of statisticians and welcoming them to the field. Outreach programs can also help enhance future diversity in the field by raising awareness of opportunities in statistics among historically underrepresented groups. As statistics, data science, and coding become more prevalent in pre-college curriculum, it is important for the future of the field for students interested in these areas to identify them with careers in statistics. In this session, you will hear from three statisticians about their experiences with a variety of local and national outreach programs, including StatFest, a STEM Incubator Program, the ENAR Diversity Workshop, and Florence Nightingale Day for Statistics and Data Science. Speakers will describe the goals and target audiences of each program, how the programs were developed, and the impact of these programs on the field. The speakers will share how you or your students can get involved in existing programs and will offer insights and advice on planning and developing new programs, including organizational and logistical lessons learned.


Dr. Brittney Bailey is an Assistant Professor of Statistics at Amherst College. She graduated with a BA in Mathematics from Messiah College and completed her PhD in Biostatistics at The Ohio State University. Her research explores statistical methods for dealing with missing data and clustered clinical trials, and her collaborations have focused on social and behavioral interventions to improve mental health and well-being. As a Black woman in STEM and a first-generation scholar from a low-income background, Dr. Bailey is dedicated to supporting students with similar experiences and works to create environments where all students can thrive.


Dr. Dr. David Kline is an Assistant Professor in the Department of Biostatistics and Data Science in the Division of Public Health Sciences at Wake Forest University School of Medicine. He has a secondary faculty appointment in the Department of Epidemiology and Prevention and an Affiliate Faculty appointment in the Department of Statistical Sciences. Dr. Kline also co-leads the cross-campus collaborative Spatial and Environmental Statistics in Health Lab. Additionally, he is a member of the Board of Directors for Florence Nightingale Day for Statistics and Data Science and a co-organizer of Florence Nightingale Day at Wake Forest University.


Dr. Kay See Tan is an associate attending biostatistician at Memorial Sloan Kettering Cancer Center, where she serves as the biostatistician for the Department of Anesthesiology and Critical Care as well as the Thoracic Surgery Service. She is also a member of the MSK Cellular Therapeutic Center focused on the designs and analyses of clinical trials utilizing CAR T cells and other immunotherapies for solid tumors. Dr. Tan is actively involved in the areas of statistical literacy and statistics education and co-directs the department’s Quantitative Science Undergraduate Research Experience (QSURE) summer internship program. She also serves as the associate director of the Biostatistics, Epidemiology and Research Design (BERD) Core at the Weill Cornell Clinical and Translational Science Center.

Register for Webinar on 6/7/2024.


When to Include the Outcome in Your Imputation Model: A Mathematical Demonstration and Practical Advice

Friday, April 12, 2024
10 AM – 12 PM

Lucy D’Agostino McGowan, Wake Forest University
Frank Harrell, Vanderbilt University School of Medicine


Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. This talk will investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e., single imputation with a fixed value) and stochastic imputation (i.e., single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Likewise, we mathematically demonstrate that including the outcome variable in imputation models when using deterministic methods is not recommended, and doing so will induce biased results. A discussion of these results along with practical advice will follow.


Lucy D’Agostino McGowan is an assistant professor in the Department of Statistical Sciences at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on analytic design theory, statistical communication, causal inference, and data science pedagogy. She can be found blogging at livefreeordichotomize.com, on Twitter @LucyStats, and podcasting on the American Journal of Epidemiology partner podcast, Casual Inference.


Dr. Frank E. Harrell received his PhD in Biostatistics from UNC in 1979. Since 2003 he has been Professor of Biostatistics, Vanderbilt University School of Medicine, and was the department chairman from 2003-2017. He is Expert Biostatistics Advisor to FDA CDER and was Expert Biostatistics Advisor for the Office of Biostatistics for FDA CDER from 2016-2020. He is Associate Editor of Statistics in Medicine. He is a Fellow of the American Statistical Association and winner of the Association’s WJ Dixon Award for Excellence in Statistical Consulting for 2014. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, cardiovascular research, technology evaluation, pharmaceutical safety, Bayesian methods, quantifying predictive accuracy, missing data imputation, and statistical graphics and reporting.

Purchase Webinar Recording (4/12/24).

Previous Webinars & Recordings