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.
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.
Mentoring in Statistical Writing: Tools for Teaching and Energizing the Statistical Writing of your Mentees
Wednesday, August 21, 2024
1-3 pm EDT
Speakers:
Dr. Nicole Dalzell, Wake Forest University
Dr. Tanya Garcia, UNC Chapel Hill
Abstract:
There are many different mentoring roles that involve teaching mentees to write or refine scientific writing skills, but individuals with training in statistics may not have training in teaching others to write. The goal of the tutorial is to provide guidance, practical tips, and teaching tools for mentoring in statistical writing. We will also discuss ways to incorporate mentoring and teaching about scientific writing in statistics into your work with mentees in a sustainable way. In this tutorial, we will:
Bios:
Dr. Nicole Dalzell is an Associate Teaching Professor in the Department of Statistical Sciences at Wake Forest University. She earned her Ph.D. in Statistics from Duke University, where she developed methods for record linkage with error prone linking variables. Currently, her work focuses on developing new pedagogical tools and techniques to help undergraduate and graduate students hone their statistical communication skills, as well as creating tools to help educators teach statistical writing. She has published in peer-reviewed statistical and data science education journals, and given presentations on teaching statistical writing, as well as creating inclusive classroom environments with specific focus on supporting students with disabilities and learning differences.
Dr. Tanya Garcia is an Associate Professor in the Department of Biostatistics and a Provost Distinguished Leader at UNC Chapel Hill. Dr. Garcia is passionate about training the next generation of (bio)statisticians to confidently develop statistical methods and communicate those methods in a clear and simple way. How she mentors this next generation is largely motivated by 500+ hours of grantsmanship and leadership training. She teaches her mentees to embrace a growth mindset and tackle obstacles without judgment or fear. Her desire for every mentee to achieve success and fulfillment drives her every leadership decision. These decisions have led Dr. Garcia to not only earn multiple grants as Principal Investigator from the National Institutes of Health, but also help other trainees and faculty members win their own grants from the National Institutes of Health, the National Science Foundation, and other non-profit organizations. Dr. Garcia was recently awarded the 2024 Landis Award for Outstanding Mentorship, an annual award from the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH), named in honor of former NINDS Director Dr. Story Landis.Outside of UNC, she leads initiatives for national statistics organizations that foster the success of underrepresented and early career (bio)statisticians.
Registration is closed. Webinar access instructions are being sent to participants via Zoom. Please contact enar@enar.org, if you require assistance.
Robust Methods for Surrogate Marker Evaluation
Friday, September 6, 2024
11 AM – 12 PM
Speaker:
Layla Parast, PhD, Associate Professor
Department of Statistics and Data Sciences, University of Texas at Austin
Abstract:
For many clinical outcomes, randomized clinical trials to evaluate the effectiveness of a treatment or intervention require long-term follow-up of participants. In such settings, there is substantial interest in identifying and using surrogate markers - measurements or outcomes measured at an earlier time or with less cost that are predictive of the primary clinical outcome of interest - to evaluate the treatment effect. Several statistical methods have been proposed to evaluate potential surrogate markers including parametric and nonparametric methods to estimate the proportion of treatment effect explained by the surrogate, methods within a principal stratification framework, and methods for a meta-analytic setting i.e., where information from multiple trials is available. While useful, these methods generally do not address potential heterogeneity in the utility of the surrogate marker. In addition, available methods do not perform well when the sample size is small. In this talk, I will discuss various robust methods for surrogate marker evaluation including methods to examine and test for heterogeneity, and methods developed for the small sample setting. These methods will be illustrated using data from an AIDS clinical trial and a small pediatric trial among children with nonalcoholic fatty liver disease.
Bio:
Layla Parast is an Associate Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. Her statistical research has focused on developing robust methods to evaluate surrogate markers, robust estimation of treatment effects, and developing and evaluating risk prediction procedures for long term survival. Her applied research has focused on measuring and comparing health care quality, and survey design and analysis for health care related surveys in a variety of settings including the emergency department, inpatient hospital, hospice, and pediatric setting. Prior to joining UT Austin, she was a senior statistician at the RAND Corporation and co-director of RAND's Center for Causal Inference.
Register for Webinar on 9/6/2024.
