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.
*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 email@example.com if you have any questions.
Novel Applications of Real-world Data to Support Clinical Trials
Friday, June 11, 2021
1 p.m. to 3 p.m. ET
Ram Tiwari, PhD
Bristol Myers Squibb
Ram C. Tiwari, Ph.D. is the Head of Statistical Methodology at BMS since February 1, 2021. His prior services include serving as Director of Division of Biostatistics at CDRH (2016-2020), Associate Director for Science and Policy in the Office of Biostatistics, CDER (2006-2016) at FDA, Mathematical Statistician and Program Director at NCI/NIH (2000-2006), and Professor and Chair of the Department of Mathematics at the University of North Carolina at Charlotte (1986-2000). He received his MS and PhD degrees from Florida State University in Mathematical Statistics. He is a Fellow of the American Statistical Association and a past President of the International Indian Statistical Association. Dr. Tiwari has over 200 publications on statistical methods, and a forthcoming book on “Signal Detection for Medical Scientists: Likelihood ratio Test-based Methodology” published by Francis &Taylor.
Wendy Wang, PhD
Wendy Wang is a Quantitative Scientist at Flatiron Health, where her research focuses on leveraging real-world data to improve cancer care among patients. Her work extends across various areas, including enhanced survival extrapolation, racial disparities in treatment and end-of-life treatment in cancer care. Prior to joining Flatiron, she received her PhD in Epidemiology from the University of Washington in 2017, and completed her post-doctoral training at Fred Hutch, with a focus in statistical genetics and cancer epidemiology.
Devin Incerti, PhD
Devin Incerti is a Principal Data Scientist at Genentech. He received his PhD from Princeton University’s School of Public and International Affairs and worked as an economist specializing in estimating the value of health technologies prior to joining Genentech. He enjoys working across disciplines and has collaborated with researchers in many fields such as bioinformatics, medicine, statistics, computer science, epidemiology, economics, and political science. His research interests generally lie in the application and development of quantitative methods and software for problems in healthcare. He is currently working on a number of topics related to health technology assessment and analyses of real-world data, including software for health economic simulation modeling, causal inference methods for supplementing randomized and single arm clinical trials with observational data, and prognostic survival modeling with high dimensional data.
Katherine Tan, PhD
Katherine Tan, PhD is a Quantitative Scientist at Flatiron Health. She is currently leading projects in real-world control arms, hybrid control arms, endpoints, and imaging (scans), where her work has highlighted ways to apply robust statistical design thinking when working with heterogeneous observational data sources such as real-world healthcare data. She received her PhD in Biostatistics from the University of Washington, Seattle.
Real-world data (RWD) have played an increasingly important role in healthcare decisions, for example supporting the design, analysis, and contextualization of clinical trials. In this webinar, we invite panelists from industry with backgrounds in statistics, clinical trials, data science, epidemiology, and health outcomes research to discuss novel applications where RWD can be used to support clinical trials.
We discuss propensity-score based methods to leverage RWD as an external data source to augment single-arm clinical trials, enhanced extrapolation of long-term clinical trial survival outcomes using electronic health record (EHR)-derived RWD, and enrollment projection for a prospective pragmatic trial design that utilizes RWD. Finally, we tie the three topics together with a panel discussion.