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Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS
436
by Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne
Douglas Faries
Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS
436
by Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne
Douglas Faries
eBook
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Overview
Discover best practices for real world data research with SAS code and examples
Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient.
The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include:
- propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods
- methods for comparing two interventions as well as comparisons between three or more interventions
- algorithms for personalized medicine
- sensitivity analyses for unmeasured confounding
Product Details
| ISBN-13: | 9781642958003 |
|---|---|
| Publisher: | SAS Institute |
| Publication date: | 01/15/2020 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 436 |
| Sales rank: | 875,371 |
| File size: | 8 MB |
About the Author
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.
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.
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.
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. Bob was elected as a Fellow of American Statistical Association (ASA) in 1997. He has also served on a variety of statistical methodological roles for SAMSI, NISS, and FNIH, and as an adjunct Professor of Biostatistics at the University of North Carolina and the Indiana University Medical School.
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. Dr. Haro is principal investigator of one of the groups of the CIBERSAM network. In 2011, he received the award of best researcher from the Spanish Society of Biological Psychiatry. In 2018, he received the award of professional excellence of the Barcelona Medical Association. He is currently the European coordinator of the EU-funded project ATHLOS and SYNCHROS and was coordinator of the Roadmap for mental health and well-being research in Europe (ROAMER).
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.
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.
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.
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. Bob was elected as a Fellow of American Statistical Association (ASA) in 1997. He has also served on a variety of statistical methodological roles for SAMSI, NISS, and FNIH, and as an adjunct Professor of Biostatistics at the University of North Carolina and the Indiana University Medical School.
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. Dr. Haro is principal investigator of one of the groups of the CIBERSAM network. In 2011, he received the award of best researcher from the Spanish Society of Biological Psychiatry. In 2018, he received the award of professional excellence of the Barcelona Medical Association. He is currently the European coordinator of the EU-funded project ATHLOS and SYNCHROS and was coordinator of the Roadmap for mental health and well-being research in Europe (ROAMER).
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