Real World Health Care Data Analysis
Causal Methods and Implementation Using SAS
Discover best practices for real world data research with SAS code and examplesReal 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 be…
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Produktdetails
Weitere Autoren: Zhang, Xiang / Kadziola, Zbigniew / Siebert, Uwe / Kuehne, Felicitas / Obenchain, Robert L / Haro, Josep Maria
- ISBN: 978-1-64295-800-3
- EAN: 9781642958003
- Produktnummer: 34848788
- Verlag: SAS Institute
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 436 S.
- Plattform: EPUB
- Masse: 8'657 KB
Über den Autor
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.
3 weitere Werke von Douglas Faries:
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