Felicitas Kuehne

Felicitas Kuehne
Senior Scientist, UMT-Austria 

Felicitas Kuehne is a Senior Scientist in Health Decision Science and Epidemiology and Coordinator of the Program on Causal Inference in Science at the Department of Public Health, Health Services Research and Health Technology Assessment at UMIT in Austria. She conducts decision-analytic modeling studies for causal research questions in several disease areas and teaches epidemiology and causal inference. She is the Coordinator of the HTADS course “Causal Inference in Observational Studies and Clinical Trials Affected by Treatment Switching: A Practical Hands-on Workshop.”

Felicitas completed her Master of Science in Health Policy and Management at the Harvard School of Public Health in 2001. From 2001 to 2011, she worked as a consultant for pharmaceutical companies, conducting several cost-effectiveness analyses in a variety of disease areas. She joined UMIT in 2011 and is currently enrolled in the doctoral program in Public Health.

Her research interests include decision-analytic modeling and health technology assessment (HTA), outcomes research, personalized medicine, and causal inference methods and applications. She has worked in cardiovascular disease, cancer, infectious diseases including HIV/AIDS and hepatitis C, and others.

By This Author

Felicitas Kuehne | 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.