Uwe Siebert
Professor of Public Health
Prof. Uwe Siebert, MD, MPH, MSc, ScD is a Professor of Public Health, Medical Decision Making and Health Technology Assessment, and Chair of the Department of Public Health, Health Services Research and HTA at UMIT – University for Health Sciences, Medical Informatics and Technology in Austria. He is also Adjunct Professor of Health Policy and Management at the Harvard Chan School of Public Health. His research interests include applying evidence-based causal methods from epidemiology and public health in the framework of clinical decision making and Health Technology Assessment. In 2004, he performed the first application of Robins’ g-formula. His current methodological research includes combining causal inference from real world evidence with artificial intelligence and decision modeling for policy decisions and personalized medicine. His substantive research focuses on cancer, cardiovascular disease, diabetes, infectious disease, neurological disorders, and others. He teaches courses at several universities in Europe, USA, South America, and Asia. Prof. Siebert has worked with several HTA agencies and advises government agencies, academic institutions and industry regarding methods for causal evaluation and HTA. He has authored more than 350 publications and is Editor of the European Journal of Epidemiology. |
By This Author
Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS®
Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.
The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.
Featured Authors
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Terisa Roberts is a director and Global Solution lead for Risk Modeling and Decisioning at SAS. She has extensive experience in quantitative risk management, regulatory compliance, and model governance and validation. She has worked in financial services, telecommunications, government, energy, and retail sectors.
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Robert N. Rodriguez retired from SAS in 2018 after serving as a senior director in research and development. Bob received a PhD in statistics from the University of North Carolina at Chapel Hill and was a research statistician at General Motors Research Laboratories before joining SAS.
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Ron Klimberg, PhD, is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA.
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Todd Case has been working in the pharmaceutical industry as a statistical programmer for over 20 years. He completed his MS in Epidemiology from Columbia University and has worked at Forest Laboratories (now Allergan), Genentech, Bristol-Myers Squibb, and Biogen. He currently works at Vertex Pharmaceuticals.