have at least an intermediate level of SAS and statistical experience. Our materials are not intended for novice users of SAS, and readers will be expected to have basic skills in data handling and analysis. However, readers will not need to be expert SAS programmers as many of our methods use standard SAS/STAT procedures and guidance is provided on the use of our SAS code.
What Should You Know about the Examples?
Almost every chapter in this book includes examples with SAS code that the reader can follow to gain hands-on experience with these causal inference analyses using SAS.
Software Used to Develop the Book’s Content
SAS 9.4 was used in the development of this book.
Example Code and Data
Each of the examples is accompanied by a description of the methodology, output from running the SAS code, and a brief interpretation of the results. All examples use one of two simulated data sets, which are available for the readers to access. While not actual patient data, these data sets are based on two large prospective observational studies and designed to retain the analytical challenges that researchers face with real world data.
You can access the example code and data for this book by linking to its author page at https://support.sas.com/authors.
Acknowledgments
We would like to thank several individuals whose reviews, advice, and discussions on methodology and data issues were critical in helping us produce this book. This includes Eloise Kaizar (Ohio State University) and multiple colleagues at Eli Lilly & Company: Ilya Lipkovich, Anthony Zagar, Xuanyao He, Mingyang Shan and Rebecca Robinson. Also, we would especially like to thank three individuals whose work helped validate many of the programs in the book: Andy Dang (Eli Lilly), Mattie Baljet, and Marcel Hoevenaars (Blue Gum Data Analysis). Without their efforts this work would not be possible.
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About the Authors
Douglas Faries graduated from Oklahoma State University with a PhD in Statistics in 1990 and joined Eli Lilly and Company later that year. Over the past 17 years, Doug has focused his research interests on statistical methodology for real world data including causal inference, comparative effectiveness, unmeasured confounding, and the use of real world data for personalized medicine. Currently, Doug is a Sr. Research Fellow at Eli Lilly, leading the Real-World Analytics Capabilities team. He has authored or co-authored over 150 peer-reviewed manuscripts including editing the textbook Analysis of Observational Healthcare Data Using SAS® in 2010. He is active in the statistical community as a publication reviewer, speaker, workshop organizer, and teaches short courses in causal inference at national meetings. He has been a SAS user since 1988.
Xiang Zhang received his BS in Statistics from the University of Science and Technology of China in 2008 and his MS/PhD in Statistics from the University of Kentucky in 2013. He joined Eli Lilly and Company in 2013 and has primarily supported medical affairs and real world evidence research across multiple disease areas. He also leads the development and implementation of advanced analytical methods to address rising challenges in real world data analysis. His research interests include causal inference in observational studies, unmeasured confounding assessment, and the use of real world evidence for clinical development and regulatory decisions. Currently, he is a Sr. Research Scientist at Eli Lilly and has been using SAS since 2008.
Zbigniew Kadziola graduated from Jagiellonian University in 1987 with an MSc in Software Engineering. Since then he has worked as a programmer for the Nuclear Medicine Department in the Silesian Center of Cardiology (Poland), Thrombosis Research Institute (UK), Roche UK, and Eli Lilly (Austria). Currently, Zbigniew is a Sr. Research Scientist at Lilly supporting the Real-World Analytics organization. He has co-authored over 40 publications and has more than 20 years of experience in SAS programming. His research focus is on the analysis of real world data using machine-learning methods.
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. 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. His current methodological research includes combining causal inference from real world evidence with artificial intelligence and decision modeling for policy decisions and personalized medicine.
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. 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.
Robert L. (Bob) Obenchain is a biostatistician and pharmaco-epidemiologist specializing in observational comparative effectiveness research, heterogeneous treatment effects (personalized/individualized medicine) and risk assessment-mitigation strategies for marketed pharmaceutical products. He is currently the Principal Consultant for Risk Benefit Statistics, LLC, in Indianapolis, IN. Bob received his BS in Engineering-Science from Northwestern and his PhD in Mathematical Statistics from UNC-Chapel Hill. Bob spent 16 years in research at AT&T Bell Labs, followed by an associate director role in non-clinical statistics at GlaxoSmithKline, before spending 17 years at Eli Lilly as a Sr. Research Advisor and Group Leader of statistical consulting in Health Outcomes Research.
Josep Maria Haro, psychiatrist and PhD in Public Health, is the Research and Innovation Director of Saint John of God Health Park in Barcelona, Spain, and associate professor of medicine at the University of Barcelona. After his medical studies, he was trained in Epidemiology and Public Health at the Johns Hopkins School of Hygiene and Public Health. Later, he got his specialization in psychiatry at the Clinic Hospital of Barcelona. During the past 25 years he has worked both in clinical medicine and in public health research and has published more than 500 scientific papers. He has been included in the list of Clarivate Highly Cited Researchers in 2017 and 2018.
Learn more about these authors by visiting their author pages, where you can download free book excerpts, access example code and data, read the latest reviews, get updates, and more:
http://support.sas.com/faries http://support.sas.com/zhang http://support.sas.com/kadziola