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 fees are by membership category, with a reduced fee for student 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 determined by membership category.

Schedule of Upcoming Webinars

The Central Role of Personalized Solution in the Era of Digital Health

Friday, January 10, 2020
10 a.m. to 12 p.m. Eastern

Haoda Fu
University of Wisconsin, Madison
Enterprise Lead of Machine Learning and Artificial Intelligence Team

Dr. Haoda Fu is a senior research advisor and a enterprise lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.

Digital health is an important pharmaceutical industry trend in recent years, and it can bring significant disruptive innovation to transform healthcare industry. In this talk, we will provide an introduction on digital health and associated analytics challenges and opportunities. In particular, we will focus on the central role of personalized intervention in the era of digital health.

Register for Webinar on 01/10/20.

Entity Resolution with Societal Impacts in Machine Learning

Friday, February 7, 2020
10 a.m. to 12 p.m. Eastern

Rebecca C. Steorts
Assistant Professor, Department of Statistical Science
Duke University

Very often information about social entities is scattered across multiple databases. Combining that information into one database can result in enormous benefits for analysis, resulting in richer and more reliable conclusions. Among the types of questions that have been, and can be, addressed by combining information include: How accurate are census enumerations for minority groups? How many of the elderly are at high risk for sepsis in different parts of the country? How many people were victims of war crimes in recent conflicts in El Salvador? In most practical applications, however, analysts cannot simply link records across databases based on unique identifiers, such as social security numbers, either because they are not a part of some databases or are not available due to privacy concerns. In such cases, analysts need to use methods from statistical and computational science known as entity resolution (record linkage or de-duplication) to proceed with analysis. Entity resolution is not only a crucial task for social science and industrial applications but is a challenging statistical and computational problem itself. In this talk, we describe the past and present challenges with entity resolution, with an application to the El Salvadorian conflict. More specifically, I will discuss unsupervised Bayesian entity resolution models, which are able to identify duplicate records in the data, while quantifying uncertainty. I will highlight the importance of choosing flexible priors and in implementing scalable inference algorithms. I will present preliminary results from the El Salvadorian conflict.

Registration coming soon!

Spatial Statistics for Disease Ecology

Friday, April 24. 2020
10 a.m. to 12 p.m. Eastern

Lance A. Waller, Ph.D.
Department of Biostatistics and Bioinformatics
Rollins School of Public Health
Emory University

Lance A. Waller, Ph.D. is a Professor in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Board on Mathematical Sciences and Analytics and has served on National Academies Committees on applied and theoretical statistics, cancer near nuclear facilities, geographic assessments of exposures to Agent Orange, and standoff explosive technologies. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Dr. Waller has also lead two separate T32 training grants, one from NIGMS and the other from NIEHS, and served as the Director of the NHLBI Summer Institute for Research Training in Biostatistics (SIBS) site at Emory for the past 9 years.

The field of disease ecology involves exploration of the multiple, dynamic interactions between pathogens, hosts, and the environment that result in the transmission of disease. Many of these interactions involve spatial or spatiotemporal components that determine the course of an outbreak and may offer potential interventions to stem the extent and duration of an outbreak at the population level. In this webinar, we provide a brief overview of the field of disease ecology, the motivating questions of interest, the nature of data involved, and the interaction of statistical and mathematical modeling addressing these questions with available data. We offer two illustrations relating to the monitoring and analysis of zoonic disease, namely: identifying geographic drivers of the spread of raccoon rabies along the Eastern United States and utilizing environmental data to enhance animal surveillance for plague in California. Both examples utilize concepts and analytic tools from spatial statistics to better understand and monitor geographically-referenced zoonotic diseases in wild animal populations.

Registration coming soon!

The Role of Statistics in Transforming EHR Data into Knowledge

Friday, May 15, 2020
10 a.m. to 12 p.m. Eastern

Rebecca Hubbard, PhD
Associate Professor of Biostatistics
University of Pennsylvania

Dr. Rebecca Hubbard is an Associate Professor of Biostatistics at the University of Pennsylvania. Her research focuses on development and application of statistical methods to improve the validity of analyses using real world data sources including electronic health records and claims data. These methods have been applied across a broad range of research areas including health services research, cancer epidemiology, aging and dementia, and pharmacoepidemiology.

The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for research on health conditions, interventions, and outcomes. Informaticians have played a leading role in the process of extracting “real world data” from EHR, with statisticians playing a more peripheral part. However, statistical insights on study design and inference are key to drawing valid conclusions from this messy and incomplete data source. This webinar will describe the basic structure of EHR data, highlight key challenges to research arising from this data structure, and present an overview of some statistical methods that address these challenges. The discussion of issues related to the structure and quality of EHR data will include: data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR and claims data; and data capture considerations such as informative visit processes and medical records coding procedures. The overall goal of this webinar is to illustrate the unique contribution of statistics to the process of generating knowledge from EHR data and equip participants with some tools for doing so.

Registration coming soon!

Previous Webinars & Recordings


Please contact Sameera Wijayawardana or Lili Zhao if you have topic suggestions for future webinars.