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

Subgroup Identification for Differential Treatment Effects

Friday, November 8, 2019
10 a.m. to 12 p.m. Eastern

Presenter:
Wei-Yin Loh
Department of Statistics
University of Wisconsin, Madison

Wei-Yin Loh is Professor of Statistics at the University of Wisconsin, Madison. He received his PhD from Berkeley in 1982. His major research interests are in bootstrap methods and classification and regression trees. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and a consultant to government and industry.

Abstract:
Many subgroup identification methods exist but they have not been compared together. To better understand the relative strengths and weaknesses of the methods, we briefly review those with publicly available software (FindIt, GUIDE, Interaction Trees, MOB, Outcome Weighted Estimation, PRIM, ROWSi, Sequential Batting, SIDES, and Virtual Twins) and then compare their performance on seven criteria: (i) variable selection bias, (ii) probability of false discovery, (iii) probability of correct variable identification, (iv) bias in subgroup treatment effect estimates, (v) expected subgroup size, (vi) expected size of subgroup treatment effects, and (vii) subgroup stability. We conclude with a bootstrap solution to performing post-selection inference on the selected subgroups.

Register for Webinar on 11/8/19


New Statistical Learning Methods for Optimizing Dynamic Treatment Decision Rules Leading Toward Personalized Health Care

Friday, December 6, 2019
10 a.m. to 12 p.m. Eastern

Presenter:
Lu Wang
Department of Biostatistics
University of Michigan

Dr. Lu Wang is Associate Professor of Biostatistics at the University of Michigan, Ann Arbor, Associate Editor for the Journal of the American Statistical Association. She received her Ph.D. in Biostatistics from Harvard University in 2008 and joined the faculty at the University of Michigan in the same year. Dr. Wang's research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, nonparametric and semiparametric regressions, missing data analysis, functional data analysis, and longitudinal (correlated/clustered) data analysis. She has been collaborating with investigators at M.D. Anderson Cancer Center, University of Michigan Medical School, and Harvard School of Public Health during the past 12 years.

Abstract:
In this talk, we present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.

Register for Webinar on 12/6/19


Entity Resolution with Societal Impacts in Machine Learning

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

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

Abstract:
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!


The Role of Statistics in Transforming EHR Data into Knowledge

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

Presenter:
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.

Abstract:
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.