Principal Consultant, Risk Benefit Statistics, LLC
A SAS user since 1986, Robert Obenchain is Principal Consultant at Risk Benefit Statistics LLC in Carmel, Indiana; Research Fellow at the National Institute of Statistical Sciences in Research Triangle Park, North Carolina; and Adjunct Professor in Biostatistics at Indiana University Medical School in Indianapolis. Previously, Dr. Obenchain worked for thirty-seven years as a professional statistician in the telecommunications (Bell Labs) and pharmaceutical industries (Eli Lilly and Glaxo) doing data analyzes, statistical computing, and methods development. He received his PhD in mathematical statistics from the University of North Carolina at Chapel Hill.
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.
Analysis of Observational Health Care Data Using SAS®
*Replaced by a new edition, Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS®