Uwe Siebert

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.

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Uwe Siebert | SAS Support

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.