ENAR 2025 Educational Program | ROUNDTABLES
Roundtables are conversations centered around a predetermined topic and led by a discussion leader. Due to the small group size, all attendees are able to participate equally, providing a more intimate discussion than a larger scientific session. All Roundtables take place Monday, March 11 from 12:15 pm – 1:30 pm.
Monday, March 24 | 12:15 pm – 1:30 pm
RT1 | Academics, Government, or Industry: Where should I go?
This interactive roundtable discussion will explore the diverse career opportunities available to statisticians in academia, government, and industry. Drawing on my firsthand experience across these sectors, I will share insights into the unique challenges, rewards, and opportunities each path presents.
Attendees are encouraged to bring questions about career choice, job search strategies, skill development, work-life balance, and other relevant topics. This roundtable provides a valuable platform for networking, building relationships, and gaining practical advice for career development. Additionally, we will discuss how to navigate transitions between sectors and leverage your skills to maximize career growth.
Leader: Eunhee Kim, Bristol Myers Squibb
Dr. Eunhee Kim is Director of Biostatistics at Bristol Myers Squibb. In her role, she leads statistical support for Oncology drug development. Prior to joining Bristol Myers Squibb, Dr. Kim served as a Principal Scientist at Merck, focusing on late-stage Oncology clinical trials. Her career began as a faculty member in the Department of Biostatistics at Brown University. Subsequently, she worked at the National Institutes of Health. She holds a PhD in Biostatistics from the University of North Carolina at Chapel Hill. Her research interests include dose optimization methods, adaptive trial design method, and statistical methods in cancer research
Monday, March 24 | 12:15 pm – 1:30 pm
RT2 | Conversations About Teaching Biostatistics to Non-Statisticians in the Eras of Big Data, Machine Learning and AI
With the fields of statistics and biostatistics being ever-evolving, and the increasing availability of data storage and computing power, it is worth pausing occasionally to think about what should be taught in introductory biostatistics courses. Certainly, the fundamental classic topics continue to have relevancy, but making the connections between these and modern-day methodologies is worth pondering. Similarly, the advent of AI and user friendly interfaces (Chat GPT etc.) has raised a lot of questions about its negative effects on being able to assess student progress, but there are also potential advantages to embracing AI for student use and assessment development.
The goal of this roundtable is to bring together statisticians interested in the education of non-statisticians to discuss these topics and others.
Leader: John McGready, Johns Hopkins
John McGready is a Teaching Professor in The Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He developed and taught the Department's first online course series in 2001, Statistical Reasoning in Public Health, and continues to teach this (average enrollment 250), and a corresponding in-person version (average enrollment 125). He also teaches a synchronous hybrid format one-term statistical methods course which includes instruction in both Stata and R (average enrollment 350). During the height of Covid McGready also taught synchronous weeklong Stata computing workshops completely online. Since 2019, he has run the Biostatistics in Public Health specialization in Coursera.
Monday, March 24 | 12:15 pm – 1:30 pm
RT3 | Methodological Challenges in Surrogate Marker Evaluation and Use
Novel biomarkers have great potential to dramatically change the decision-making process of modern medicine. In particular, there is strong interest in using markers as a surrogate for assessing a treatment effect, for diagnosing disease or for predicting the risk of future clinical events. However, there are many methodological challenges in evaluating surrogate markers and using them in a future clinical trial to make a decision about a treatment. In this roundtable, we will discuss available methods to evaluate potential surrogates, advantages and disadvantages of the available methods, and practical use. We will also discuss the challenges that come with potential heterogeneity in the utility of a surrogate i.e., when a surrogate may capture the treatment effect for some subgroups but not others, and the difficulty of evaluating a marker in a small sample size setting. Lastly, we will discuss how surrogates can appropriately be used in future trials, the inherent assumptions when we do use a surrogate, and open problems in this area of research.
Leader: Layla Parast, University of Texas at Austin
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 in 2022, she was a senior statistician at the RAND Corporation and co-director of RAND's Center for Causal Inference
Monday, March 24 | 12:15 pm – 1:30 pm
RT4 | Statistical Learning with Administrative Healthcare Data
The growing availability of electronic health record (EHR) data is opening new opportunities for research. EHRs are routinely collected longitudinal data containing demographics, medical diagnosis and procedure, medication, immunization, laboratory test results, radiology images, vital signs, and billing information. Ongoing efforts have been made to integrate large scale EHR data across healthcare systems, as well as to link EHR data with biobank, insurance claims, registries, and death indices. With such cost-effective data sources, the health of an individual is now characterized with unprecedented precision and depth, facilitating contemporary research that makes the transition from data to knowledge. This roundtable will offer a discussion of modern analytical methods to address challenges in research using EHR data. Participants will gain a broader understanding of the opportunities and challenges of using EHR data for research and be prepared to explore new questions, perspectives, and methodological advancements.
Leader: Xu Shi, University of Michigan
Xu Shi is an Associate Professor in the Department of Biostatistics at the University of Michigan. She is interested in developing statistical methods for electronic health records and claims data, focusing on causal inference, data harmonization across healthcare systems and comparative effectiveness and safety research.
Monday, March 24 | 12:15 pm – 1:30 pm
RT5 | Statistical Challenges for Environmental Mixtures
Humans interact with a mixture of environmental exposures daily. These exposures are known to play important roles in the etiology of many diseases, including cancer, respiratory and autoimmune diseases, and reproductive disorders, and can serve as strong predictors of disease incidence and progression. Recent methodologic advances enable researchers to analyze these exposures simultaneously to reveal the complex relationship between mixtures and health outcomes, which is superior to focusing on single, isolated exposures. Carefully designed population-based studies and sophisticated statistical and computational tools are needed for this purpose. In this roundtable, we will discuss recent advancements and current challenges for analyzing mixtures data.
Leader: Shanshan Zhao, NIEHS/NIH
Dr. Shanshan Zhao is a Senior Investigator in the Biostatistics and Computational Biology Branch, NIEHS/NIH. She holds a secondary appointment in the NIEHS Epidemiology Branch, and an adjunct associate professorship at the Department of Biostatistics, University of North Carolina at Chapel Hill. The overarching goal of Dr. Zhao's research is to develop novel statistical methods to discover how humans’ interaction with the physical and social environment influences their health and well-being. She and her group integrate statistical methodological developments and collaborative population-based studies, focusing on survival and longitudinal data analysis.
Monday, March 24 | 12:15 pm – 1:30 pm
RT6 | Pandemics, Epidemics and Local Outbreaks: How Can We Contribute to Prevention of Large-scale Spread of the World’s Evolving Bad Bugs?
The last decade has demonstrated that local infectious diseases outbreaks can rapidly become global health-care crises. Viruses that cause Ebola, COVID-19, and mpox provide examples and demonstrate the value statisticians provide in response.
The West African Ebola outbreak started in a small village in Guinea in late 2013. Concern about spread was initially low—previous Ebola outbreaks suggested regional spread unlikely. Ultimately, ebolavirus expanded to five other African countries, the US, UK, Spain, and Italy, resulting in > 28,000 cases and >11,000 deaths. The ethics of conducting a randomized controlled trial (RCT) was a major debate; and, despite the number of cases, the outbreak ended with only one RCT for treatments and one cluster-RCT for vaccine. Four-years later, the world’s second largest outbreak occurred in the Democratic Republic of Congo (DRC). Within four-months, we launched the PALM001 trial that ultimately identified two life-saving drugs with a multi-arm RCT.
When cases of SARS-CoV-2 were reported in Wuhan, China in late 2019, concern about global spread quickly heightened. Increased contagiousness of respiratory viruses and severity of illness triggered immediate preparations. The Adaptive Covid-19 Treatment Trial (ACTT) remdesivir RCT opened to enrollment one month after conception and enrolled within 54 days. While there were questions about the study design, including whether the endpoint and heterogenous patient population were optimal, trial results were definitive and led to the first COVID-19 drug approval.
Monkeypox virus has been circulating in DRC, since at least 1970 when the first human case was identified in an infant in Basankusu. Outbreaks of mpox disease were thought to be contained locally, until 2022, with the spread of Clade IIb monkeypox virus, which has caused >90,000 cases globally. Prior to the global outbreak, we identified mpox as a disease of concern that lacked proven effective therapeutics and designed the PALM007 RCT. Results from that trial became available in August 2024, nearly concurrent with the WHO declaration of a public health emergency of international concern for mpox.
The disease settings above reveal lessons learned about the value of randomization, study-design challenges with imperfect knowledge about a novel disease, and the need to adapt the classic phase I/II/III paradigm. We will discuss these challenges, and the roles statisticians have in preparing for and responding to the next outbreak.
Leader: Lori Dodd, NIAID/NIH
Dr. Lori Dodd is a biostatistician and chief of the Clinical Trials Research and Statistics Branch within the Office of Biostatistics, Division of Clinical Research, National Institute of Allergy and Infectious Diseases (NIAID). Lori also leads the Pamoja Tulinde Maisha (PALM; "Together, Save Lives") infectious disease research partnership between NIAID and the Democratic Republic of Congo’s National Institute of Biomedical Research. She’s contributed to the success of multiple randomized controlled trials during infectious disease outbreaks, including the Ebola virus disease therapeutics (PREVAIL II and PALM001), COVID-19 therapeutics (Adaptive COVID-19 Treatment Trial (ACTT) 1-4), and mpox therapeutics (PALM007). Prior to joining NIAID, she worked as a mathematical statistician at the National Cancer Institute. She earned her PhD from the Department of Biostatistics at the University of Washington.
Monday, March 24 | 12:15 pm – 1:30 pm
RT7 | Statistical Challenges in Neuroimaging
Neuroimaging data is rapidly evolving, with emerging techniques for data acquisition and large-scale biobanks making massive amounts of data available to the community. Neuroimaging data has been critical in our understanding of the structure and function of the human brain, which has downstream effects in, e.g., our ability to identify biomarkers for psychiatric conditions and for developing cognitive and behavioral interventions. In this roundtable, we will give an introduction to structural and functional magnetic resonance imaging data. We will discuss key challenges and ways that statisticians can have a large impact in this area of research.
Leader: Mark Fiecas, University of Minnesota
Dr. Mark Fiecas is an Associate Professor in the Division of Biostatistics and Health Data Science at the University of Minnesota. Dr. Fiecas is a Co-Director of the Analytics Hub of the Masonic Institute for the Developing Brain, which develops novel statistical methods and provides analytic services to researchers at the University of Minnesota. Dr. Fiecas develops novel spatiotemporal methods for analyzing brain imaging data, with an emphasis on statistical methods for functional connectivity analyses. Dr. Fiecas is highly engaged in interdisciplinary research related to adolescent mental health.
Monday, March 24 | 12:15 pm – 1:30 pm
RT8 | Research, Training & Funding Opportunities at the National Institutes of Health
In this Roundtable discussion, I will cover the different kinds of funding opportunities that will be helpful in applying for research and training grants at NIH. The discussion will include on how to navigate through NIH website to find funding opportunities that are of interest to your research and training area, to discuss the grant submission process through solicited or unsolicited funding opportunities, and to go through helpful tips that can be useful in writing grant applications. Examples will be provided from the statistical methods funded grants at NIH. Junior researchers and experienced researchers are welcome to participate in the roundtable discussion.
Leader: Misrak Gezmu, National Institutes of Health
Dr. Gezmu is currently a Program Official for Statistical Methods grants in infectious diseases research at the Division of Clinical Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH). In her capacity as a Program Official, she informs the statistical community about the current research area of the institute, gives advice in submitting grant applications and discusses the next steps after their grant is reviewed. She is highly dedicated to increasing the biostatistical work force to address the growing demand of biostatisticians in biomedical research nationally and internationally. Dr. Gezmu is an elected Fellow of the American Statistical Association.
Monday, March 24 | 12:15 pm – 1:30 pm
RT9 | Statistical Issues in Generative AI
Artificial intelligence (AI) has started to revolutionize many different fields including natural language processing, image generation, and the biomedical sciences. As the influence of these technologies continues to grow, the need for robust statistical techniques has never been more important. In this roundtable, we will explore the real opportunities for statisticians and biostatisticians in generative AI research. Here, we will touch on a variety of topics including model evaluation, data augmentation, uncertainty quantification, and interpretable AI. We will also discuss some of key skillsets that are useful to make a real impact in this research space, whether it be in academia, industry, or some other sector.
Leader: Lorin Crawford, Brown University
Lorin Crawford is a Principal Researcher at Microsoft Research. He also maintains a faculty position as a Distinguished Senior Fellow of Biostatistics at the Brown University School of Public Health. The central aim of his research program is to build machine learning algorithms and statistical tools that aid in the understanding of how genetic and gene-by-environmental interactions contribute to the architecture of complex traits and disease progression. His work has landed him a place on Forbes 30 Under 30 list and recognition as a member of The Root 100 Most Influential African Americans. He also been awarded an Alfred P. Sloan Research Fellowship, a David & Lucile Packard Foundation Fellowship for Science and Engineering, and a COPSS Emerging Leader Award. Lorin is an Associate Editor at the journal Biostatistics, an Associate Editor of Reproducibility at the Journal of the American Statistical Association (JASA), and a Regional Committee member of the International Biometric Society Eastern North American Region (IBS ENAR).