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.

*ENAR has decided to waive WebENAR registration fees for current ENAR members during the pandemic. Advance registration is still required for all attendees. Please email enar@enar.org if you have any questions.

Schedule of Upcoming Webinars

Collaboration: Pairwise Survival Analysis and Causal Inference for Infectious Disease Epidemiology and Understanding Transmission Dynamics of Emerging Infectious Diseases from Contact-tracing Data

Friday, February 25, 2022
10 a.m. to 12 p.m. ET

Eben Kenah
Ohio State University

Eben Kenah is an associate professor of biostatistics in the College of Public Health at the Ohio State University in Columbus, Ohio. His research interests include statistical methods for infectious disease epidemiology, epidemiologic methods, survival analysis, causal inference, epidemic models, and networks.

Pairwise survival analysis and causal inference for infectious disease epidemiology: Causal inference for infectious disease transmission is complicated because outcomes in different individuals are inherently dependent, which leads to interference or spillover of treatment effects. For example, individuals who are not vaccinated are partly protected when individuals around them are vaccinated. An established approach to this problem is to define causal effects in populations (e.g., a vaccination program in a village) and then attempt to measure these directly. An alternative approach is to define causal effects in pairs of individuals and estimate them using methods from pairwise survival analysis. This approach is likely to yield results that generalize more easily between populations, and it allows more detailed mechanistic insight into the effects of interventions. These pairwise causal effects can be used as the basis of epidemic models that allow estimation of the causal effect of an intervention in a population. This approach places greater emphasis on the longitudinal study of transmission in close contact groups than has been evident in the ongoing COVID-19 pandemic.

Yang Yang
University of Florida

Yang Yang is an associate professor of biostatistics in the College of Public Health and Health professions as well as Emerging Pathogens Institute at the University of Florida. His research focuses on statistical methods for disease transmission dynamics, efficacy evaluation, missing data and surveillance bias. He also works on ecological modeling and genetic association for clinical outcomes.

Understanding transmission dynamics of emerging infectious diseases from contact-tracing data: Contact-tracing data provide crucial and reliable information for understanding transmissibility, risk drivers and intervention efficacies for newly emerging infectious diseases. Analysis of such data is often challenging mainly due to surveillance bias, missing data and lack of biological understanding, which have been further exacerbated by COVID-19. We examine several of these challenges: (1) diagnostic bias towards symptomatic infections; (2) presymptomatic infectivity, i.e., the latent period is shorter than the incubation period; and (3) reporting bias, where only confirmed cases are reported but uninfected close contacts remain unknown. These issues, if left unaddressed, can lead to erroneous estimation of key epidemiological parameters. I will discuss our experiences in the analysis of household transmission of SARS-CoV-2 in Wuhan, China and nosocomial transmission of MERS-CoV in the Kingdom of Saudi Arabia several. I will introduce some statistical adjustments we have adopted to address the aforementioned challenges.

Registration opens next week!


A Novel Approach for the Analysis of Randomized Clinical Trials

Friday, April 22, 2022
10 a.m. to 12 p.m.

Devan V. Mehrotra
Biostatistics and Research Decision Sciences, Merck & Co., Inc.

Devan V. Mehrotra, PhD, is Vice President, Biostatistics, at Merck Research Laboratories (MRL). Over the past 30 years, he has made significant contributions towards the research, development and regulatory approval of medical drugs and vaccines across a broad spectrum of therapeutic areas. He was awarded an MRL Presidential Fellowship in 2012. Dr. Mehrotra is also an Adjunct Associate Professor of Biostatistics at the University of Pennsylvania and an elected Fellow of the American Statistical Association. He has served as a subject matter expert for the Bill and Melinda Gates Foundation, the US National Academy of Sciences, the Coalition for Epidemic Preparedness Innovations, and the International Council on Harmonization. His current research focus is on statistical innovation for enabling personalized medicine.

Randomized clinical trials use either stratified or unstratified randomization. For the former, the stratification factors are typically categorical baseline covariates (region, age group, ECOG status, etc.) that are presumed to influence the clinical endpoint of interest. We caution that uncertainty at the trial design stage can contribute to "ineffective" stratification and the corresponding stratified analysis can lead to an adversely biased or imprecisely estimated treatment effect, especially for trials designed to assess whether a test treatment prolongs survival relative to a control treatment. To mitigate this non-trivial risk, we show how “effective” stratification can be achieved using a pre-specified treatment-blinded algorithm applied to the clinical trial outcomes, followed by a power-boosting stratified analysis after treatment unblinding. We illustrate the utility of our proposed ‘5-STAR’ approach relative to current practice using a graphical summary of p-values and hazard ratio estimates from 23 real data examples. We also discuss alignment of our novel proposal with FDA guidance on covariate-adjusted analyses, and with related publications by John Tukey, Stuart Pocock, and others. (An R package to implement 5-STAR is available at https://github.com/rmarceauwest/fiveSTAR)

Registration opens in February!


Previous Webinars & Recordings


Please contact Samson Ghebremariam or Emily Slade if you have topic suggestions for future webinars.