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.
Collaboration: Applications of RWE in drug development and methodologies for confounding control and A statistical roadmap for journey from real-world data to real-world evidence
Friday, February 28. 2020
10 a.m. to 12 p.m. Eastern
Hongwei Wang, PhD
Dr. Hongwei Wang is currently a Director of Global Medical Affairs Statistics, Data and Statistical Sciences at AbbVie. He held a PhD in Statistics from Rutgers University and his research interests include designing and analyzing real-world studies, network meta-analysis and advanced analytics. Before AbbVie, he worked at Merck and Sanofi.
Applications of RWE in Drug Development and Methodologies for Confounding Control: Real-world evidence (RWE) is playing an increasingly important role in drug development, from early in discovery throughout clinical development program to life-cycle management. RWE can augment randomized clinical trials for regulatory approval, establish the effectiveness and safety profile in routine clinical practice to support reimbursement decision, and constitute an integral part of scientific communication overall. Due to its noninterventional nature, a key challenge of robust RWE generation is to establish causal relationship between exposure and outcome. This talk focuses on several main methodologies for causal inference, consisting of IPTW, MLE, AIPTW, and TMLE using full data and matched data that is derived from propensity score matching, respectively. Following the RWE roadmap outlined in the first talk, practical considerations are given to facilitate the series of decisions for confounding control, such as defining estimand, usage of matching, and choice among different analytic frameworks.
Yixin Fang, PhD
Dr. Yixin Fang is director of Global Medical Affairs (GMA) Statistics at AbbVie. Since he joined AbbVie in January 2019, he has focused his research on real-world studies, comparative effectiveness research, and causal inference. After he received his PhD in statistics from Columbia University in 2006, he had been working in academia for 12 years, teaching young statisticians and doing research in different fields such as machine learning, high-dimensional data analysis, and big data analysis. Motivated by the research of Professor Mark van der Laan, he is promoting the applications of targeted learning in real-world data research, combining his experiences in both machine learning and causal inference.
A statistical roadmap for journey from real-world data to real-world evidence: Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. On the other hand, real-world data may be much less convincing due to the lack of randomization and the presence of confounding bias. In this presentation, we propose a statistical roadmap to translate real-world data (RWD) to robust real-world evidence (RWE). The roadmap consists of three main stations: (1) defining an estimand translating the research objective into a precise definition of the treatment effect that is to be estimated, (2) constructing an efficient estimator (minimum-variance unbiased estimator) for the estimation of the estimand, and (3) conducting sensitivity analysis to explore the robustness of the inference to deviation from the underlying no-unmeasured confounding assumption. The Food and Drug Administration (FDA) is working on guidelines, with a target to release a draft by 2021, to harmonize RWD applications and monitor the safety and effectiveness of pharmaceutical drugs using RWE. The proposed roadmap aligns with the newly released framework for FDA's RWE Program in December 2018 and we hope this statistical roadmap is useful for statisticians who are eager to embark on their journeys in the real-world research.
Spatial Statistics for Disease Ecology
Friday, April 17. 2020
10 a.m. to 12 p.m. Eastern
Lance A. Waller, Ph.D.
Department of Biostatistics and Bioinformatics
Rollins School of Public Health
Lance A. Waller, Ph.D. is a Professor in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Board on Mathematical Sciences and Analytics and has served on National Academies Committees on applied and theoretical statistics, cancer near nuclear facilities, geographic assessments of exposures to Agent Orange, and standoff explosive technologies. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Dr. Waller has also lead two separate T32 training grants, one from NIGMS and the other from NIEHS, and served as the Director of the NHLBI Summer Institute for Research Training in Biostatistics (SIBS) site at Emory for the past 9 years.
The field of disease ecology involves exploration of the multiple, dynamic interactions between pathogens, hosts, and the environment that result in the transmission of disease. Many of these interactions involve spatial or spatiotemporal components that determine the course of an outbreak and may offer potential interventions to stem the extent and duration of an outbreak at the population level. In this webinar, we provide a brief overview of the field of disease ecology, the motivating questions of interest, the nature of data involved, and the interaction of statistical and mathematical modeling addressing these questions with available data. We offer two illustrations relating to the monitoring and analysis of zoonic disease, namely: identifying geographic drivers of the spread of raccoon rabies along the Eastern United States and utilizing environmental data to enhance animal surveillance for plague in California. Both examples utilize concepts and analytic tools from spatial statistics to better understand and monitor geographically-referenced zoonotic diseases in wild animal populations.
The Role of Statistics in Transforming EHR Data into Knowledge
Friday, May 15, 2020
10 a.m. to 12 p.m. Eastern
Rebecca Hubbard, PhD
Associate Professor of Biostatistics
University of Pennsylvania
Dr. Rebecca Hubbard is an Associate Professor of Biostatistics at the University of Pennsylvania. Her research focuses on development and application of statistical methods to improve the validity of analyses using real world data sources including electronic health records and claims data. These methods have been applied across a broad range of research areas including health services research, cancer epidemiology, aging and dementia, and pharmacoepidemiology.
The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for research on health conditions, interventions, and outcomes. Informaticians have played a leading role in the process of extracting “real world data” from EHR, with statisticians playing a more peripheral part. However, statistical insights on study design and inference are key to drawing valid conclusions from this messy and incomplete data source. This webinar will describe the basic structure of EHR data, highlight key challenges to research arising from this data structure, and present an overview of some statistical methods that address these challenges. The discussion of issues related to the structure and quality of EHR data will include: data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR and claims data; and data capture considerations such as informative visit processes and medical records coding procedures. The overall goal of this webinar is to illustrate the unique contribution of statistics to the process of generating knowledge from EHR data and equip participants with some tools for doing so.