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

— —

Douglas Faries Xiang Zhang

Real World Health Care Data Analysis

Causal Methods and Implementation Using SAS

Douglas Faries, Xiang Zhang, Zbigniew Kadziola

$224.95 $179.75

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

SAS Institute
15 January 2020

Enterprise software; Mathematical & statistical software

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

By: Douglas Faries, Xiang Zhang, Zbigniew Kadziola
Imprint: SAS Institute
Dimensions: Height: 279mm, Width: 216mm, Spine: 24mm
Weight: 1.288kg
ISBN: 9781642958027
ISBN 10: 1642958026
Pages: 436
Publication Date: 15 January 2020
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active

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.