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.
Registration fees are determined by membership category.
Programming with hierarchical statistical models:
An introduction to the BUGS-compatible NIMBLE system for MCMC and more
Optimal Bayesian Analysis of A/B Tests (Randomized Controlled Trials) in Data Science at Big-Data Scale
Friday, May 18, 2018
10:00 am – 12:00 pm Eastern
Presenter:
Dr. David Draper
Professor, Applied Mathematics & Statistics
University of California, Santa Cruz
Description:
In this talk I'll identify an important class of problems arising in the analysis of randomized controlled trials (typically called A/B tests in Data Science) in which optimal Bayesian analysis is made possible by the use of Bayesian non-parametric modeling, and I'll illustrate this type of analysis with an A/B test at Big-Data scale (involving about 22 million observations). Along the way I'll demonstrate that the frequentist bootstrap is actually a Bayesian non-parametric method in disguise. Please click here to view full abstract.
Click Here to Register (May 18)
Must Register before May 17. nstructions for access will be e-mailed to all participants on May 17.
Evidence Synthesis for Clinical trials: Use of Historical Data and Extrapolation
Friday, June 22, 2018
10:00 am – 12:00 pm Eastern
Presenters:
Sebastian Weber
Associate Director SMC
Novartis Pharma AG
Satrajit Roychoudhury
Senior Director
Pfizer Inc.
Description:
A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012), occasionally in phase III trials (French et al., 2012), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989). This allows trials with smaller sample size or with unequal randomization (more subjects on treatment than control). In addition, the Bayesian statistical paradigm is a natural approach for combining information across heterogeneous sources, such as different trials or the adult and pediatric data. In this webinar we'll provide a statistical framework to incorporate trial external evidence with real life examples.
During the first part of the webENAR we will introduce the meta-analytic predictive (MAP) model (Neuenschwander, 2010). The MAP model is a Bayesian hierarchical model which combines the evidence from different sources (usually studies). The MAP model provides a prediction for a future study based on available information while accounting for inherent heterogeneity in the data. This approach can be used widely in different applications of biostatistics.
In the second part of the webENAR we will focus on three key applications of the MAP approach in biostatistics, which are (i) the derivation of informative priors from historical controls, (ii) probability of success and (iii) extrapolation from adult data to pediatrics. These applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. The aim of the webinar is to teach the MAP approach and enable participants to apply the approach themselves with the help of RBesT.
Click Here to Register (June 22)
Must Register before June 21. Instructions for access will be e-mailed to all participants on June 21.
Sensitivity analysis in observational research: introducing the E-value
Friday, September 28, 2018
10:00 am – 12:00 pm Eastern
Presenter:
Dr. Tyler VanderWeele
Professor of Epidemiology
Harvard School of Public Health
Abstract:
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This webinar introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The speaker and his collaborators propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Reference: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167:268-274.
Online E-value Calculator: https://mmathur.shinyapps.io/evalue/
Click Here to Register (Sept 28)
Must register before September 27. Instructions for access will be e-mailed to all participants on September 27.
Biostatistical Methods for Wearable and Implantable Technology
Friday, October 26, 2018
10:00 am – 12:00 pm Eastern
Presenter:
Dr. Ciprian Crainiceanu
Professor, Department of Biostatistics
Johns Hopkins University
Abstract:
Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.
Click Here to Register (Oct. 26)
Must register before October 25. Instructions for access will be e-mailed to all participants on October 25.
Machine Learning for Health Care Policy
Friday, November 30, 2018
10:00 am – 12:00 pm Eastern
Presenter:
Dr. Sherri Rose
Associate Professor of Health Care Policy (Biostatistics)
Harvard Medical School
Abstract:
Health care research is moving toward analytic systems that take large health databases and estimate quantities of interest both quickly and robustly, incorporating advances from statistics, machine learning, and computer science. Pressing questions in prediction and causal inference are being answered with machine learning techniques. I will give an overview of the specific challenges related to developing and deploying these statistical algorithms for health policy, including examples from the areas of health plan payment, mental health outcomes, cancer staging, and medical devices. This webinar will be accessible for graduate students with most technical derivations provided in references
Click Here to Register (Nov. 30)
Must Register before November 29. Instructions for access will be e-mailed to all participants on November 29.
Lessons and Strategies for a Career in Academia: A Conversation
Friday, December 14, 2018
10:00 am – 12:00 pm Eastern
Presenter:
Dr. Leslie McClure
Professor & Chair, Department of Epidemiology and Biostatistics
Dornsife School of Public Health at Drexel University
Dr. Elizabeth Stuart
Associate Dean for Education and Professor of Biostatistics, Mental Health, and Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
Abstract:
As a Biostatistician, there are many paths to a successful career. Each has benefits and drawbacks and will depend on an individual's own skills and preferences. In this webinar, Drs. Elizabeth Stuart and Leslie McClure will host a conversation about their academic careers, including providing some strategies for success and describing some of the challenges they've faced. They'll consider important questions, such as: What to look for in a job? How to develop meaningful collaborations (and get out of those that are not productive)? How to prioritize activities with an eye towards promotion (e.g., collaborative and methodological projects)? How to balance teaching, research, and grant requirements? And how to balance all of that with things outside of work? However, the exact direction of the conversation will depend on the questions and engagement from webinar participants.
Click Here to Register (Dec. 14)
Must register before December 13. Instructions for access will be e-mailed to all participants on December 13.
Please contact Michael Hudgens if you have topic suggestions for future webinars.
