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Analysis of Observational Health Care Data Using SAS, by Douglas Faries, Andrew C. Leon, Josep Maria Haro, Robert L. Obenchain
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This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data.
- Sales Rank: #882584 in Books
- Brand: Brand: SAS Institute
- Published on: 2010-02-26
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x 1.02" w x 8.25" l, 2.25 pounds
- Binding: Perfect Paperback
- 452 pages
Features
- Used Book in Good Condition
Review
"Analysis of Observation Health Care Data Using SAS should be a required reference book available to health outcomes, economics, and epidemiology researchers at all levels, whether they are graduate students or experienced analysts. It contains much practical wisdom; describes the techniques, standards, and pitfalls of analyzing real world data; as well as provides actual computer code that should be immediately useful to analysts of 'real world' health care data. It even has references to publications that have applied such methods. This is a long awaited and much needed book, and I am hopeful that it will serve as a general guidance to improve the quality of research using observational data." --Howard G. Birnbaum, PhD, Principal, Analysis Group, Inc.
"This book, which is a collection of articles by experts, serves a vital need for a good general book on the subject of observational studies in medicine. The randomized control trial is not the only method of analysis, but observational studies have their special problems. This book concentrates on method such as propensity scoring and instrumental variables, pointing out the advantages and disadvantages of each. The technical level varies from chapter to chapter, but is generally fairly accessible. The chapters all have extensive references, and each has copious SAS code, with comments and output, to illustrate key ideas. If you analyze observational data in the medical field, you will want this book." --Peter Flom, Independent Statistical Consultant, Peter Flom Consulting, LLC
About the Author
Douglas E. Faries is Senior Research Advisor at Lilly USA, where he oversees statistical design and analysis support for Health Outcomes Research. A SAS user for more than 20 years, he received his Ph.D. in Statistics from Oklahoma State University and his M.S. in Mathematics from Western Illinois University.
Andrew C. Leon is Professor of Public Health and of Biostatistics in Psychiatry at Weill Cornell Medical College in New York City. A SAS user since 1982, he received his Ph.D. in Educational Psychology from City University in New York.
Josep Maria Haro is Director of the Saint John of God Research and Teaching Foundation in Barcelona, Spain. He earned an M.D. from the University of Barcelona, a Master's in Public Health from Johns Hopkins University, and a Ph.D. from University Autonoma in Barcelona. He has been a SAS user since 1990.
Robert L. Obenchain is Principal Consultant at Risk Benefit Statistics LLC in Carmel, Indiana; Research Fellow at the National Institute of Statistical Sciences in Research Triangle Park, North Carolina; and Adjunct Professor in Biostatistics at Indiana University Medical School in Indianapolis, Indiana. A SAS user since 1986, he received his Ph.D. in Mathematical Statistics from the University of North Carolina at Chapel Hill
Most helpful customer reviews
2 of 2 people found the following review helpful.
Adequate analysis text, but SAS does not belong in the title.
By Keith Carnes
This book would serve as a reasonable text for a class on retrospective health data analysis but is woefully short of meaningful programming instructions to support the subject matter. Plus, the code that is offered is frequently cluttered with print formatting instructions that have nothing to do with the content the book should be delivering.
5 of 6 people found the following review helpful.
Really great book!
By anonymous
I am a masters level epidemiology/biostatistics data analyst and I took a job with a pharmaceutical company doing health claims analyses that are mostly focused on pharmacoepidemiology and health economics. I did not have any specific experience with these types of analyses and this book was a great introduction! Like another reviewer mentioned, it is focused heavily on propensity scores. Each chapter has an applied example and includes all or most of the SAS code required to do the analysis. The chapters are very focused on application and not too too much on the theoretical (as many stats books tend to be). There are sections on claims analysis and pharmacoeconomic analyses, such as cost-effectiveness and incremental net benefit analysis.
As far as the level of understanding, this book does assume a biostats and SAS foundation. I thought the authors did a great job at making the book fairly easily comprehended by a masters level biostats person like myself, even though many of these analytic methods are beyond what was explicitly covered in my coursework. I just mention this because I am sometimes unsure about the level of knowledge assumed by these books before purchasing them (you really can't tell by reading a sample of the first chapter, which is almost always extremely introductory!). I have been disappointed by some other SAS publications (eg, healthcare data analysis & SAS) that turn out to be way below my level of understanding and I don't come out with much new, useful knowledge.
GREAT, GREAT BOOK! I have already recommended it to some of my colleagues.
0 of 1 people found the following review helpful.
Really useful and practical
By Sophia
This book was really invaluable in walking me through doing a propensity score analysis. The writing was clear and enjoyable to read, and the sample code was complete and all I needed to get through my analysis. I have about a year's worth of graduate level biostatistics/clinical research training, and would count myself as advanced beginner in SAS, and this book was easily understandable with my level of background. The examples are really useful and relevant to the medical field, and complete! For example,not only do they cover many different methods of propensity score calculation and matching, they then go on to cover all the different types of analyses you can do AFTER you've calculated and matched on your propensity score (e.g., not just how to incorporate propensity scores into logistic regression analyses, but also cox proportional hazards, kaplan-meier curves, etc.) so that you aren't left hanging.
This book achieves that rare balance between readability/accessibility, ease of finding exactly what you want, and completeness.
See all 6 customer reviews...
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