Real World Health Care Data Analysis: Causal Methods and Implementation Using SASŪ: Causal Methods and Implementation Using SASŪ

Faries, Douglas; Zhang, Xiang; Kadziola, Zbigniew; Siebert, Uwe; Kuehne, Felicitas; Obenchain, Robert; Haro, Josep Maria

ISBN 10: 1642957984 / ISBN 13: 9781642957983

Published by SAS Institute, 2020

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Bibliographic Details

• Title: Real World Health Care Data Analysis: Causal Methods and Implementation Using SASŪ: Causal Methods and Implementation Using SASŪ
• Publisher: SAS Institute
• Publication Date: 2020
• Language: English
• ISBN 10: 1642957984
• ISBN 13: 9781642957983
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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 SASŪ 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

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.