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.
- Dr. Christian FG Schendera is a Senior SAS Data Scientist and managing director at Method Consult in Switzerland. An avid SAS user for 30+ years, his experience ranges from scientific consulting, project management, and feature-engineering to statistical modeling using SAS.
- Dr. Sharad Saxena is a Principal Analytical Training Consultant based at the SAS R&D center in Pune, India. Working in the field of statistics and analytics since 2020, he provides education consulting in the area of advanced analytics and machine learning across the globe.
- Michael Patetta has been a statistical instructor for SAS since 1994. He teaches a variety of courses including Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio, Predictive Modeling Using Logistic Regression, Introduction to Data Science Statistical Methods, and Regression Methods Using SAS Viya.
- Neil Constable was a Principal Education Consultant at SAS Institute in the United Kingdom, where he has worked since 2002.