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As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat G�nen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Topics addressed include: Appropriate methods for binary, ordinal, and continuous measures, Computations using PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros, Comparing the ROC curves of several markers and adjusting them for covariates, ROC curves with censored data, Using the ROC curve for evaluating multivariable prediction models via bootstrap and cross-validation, ROC curves in SAS Enterprise Miner, And more! Written for any statistician interested in learning more about ROC curve methodology, the book assumes readers have a basic understanding of regression procedures and moderate familiarity with Base SAS and SAS/STAT. Some familiarity with SAS/GRAPH is helpful but not essential.
- Sales Rank: #1053730 in Books
- Published on: 2007-10-10
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x .35" w x 8.50" l, .80 pounds
- Binding: Paperback
- 152 pages
Review
"Mithat G�nen has produced the best text to date regarding receiver operating characteristic (ROC) curves and their use in statistical analysis. He provides insight into how ROC curve analysis can be used to assess the accuracy of predictions and forecasts. The book is easy to read and comprehend and has a wealth of SAS programming examples from business, science, and medicine. The author covers all the essential approaches to analyzing receiver operating characteristic curves. I highly recommend this book!" --William F. McCarthy, Ph.D., Director, Clinical Trials,Clinical Statistics and SAS Programming, Maryland Medical Research Institute, Baltimore, MD
"ROC curves may seem quite mysterious at first; what does a "receiver operating characteristic curve" have to do with biological or biomedical phenomena? Dr. G�nen's lucid and well-organized book dispels the ROC mystery and enables ready adoption of this exciting methodology. The mixture of clearly articulated theory and development of topics with SAS code and review of SAS output throughout the text is very effective for the introduction of this material the examples are focused and well-designed. In sum, I heartily recommend Dr. G�nen's fine book." Robert Singer, M.S. PDL BioPharma --Robert Singer, M.S., PDL BioPharma
About the Author
Mithat G�nen is an Associate Attending Biostatistician at Memorial Sloan-Kettering Cancer Center, where he specializes in clinical trial design, medical diagnostic tests, and predictive model development. A SAS user since 1991, he is the author of numerous professional papers and a member of the American Statistical Association and the International Biometric Society. Mithat received a B.S. in Electrical Engineering from Middle East Technical University, an M.S. in Industrial Engineering from Stanford University, and an M.S. and Ph.D. in Statistics from Texas Tech University.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Excellent!
By Wine Country Guy
Excellent book addressing core aspects of the predictive modeling process that are often overlooked by practitioners. Examples and code samples are clear, forthright and easy to apply in your own work with SAS Software.
1 of 2 people found the following review helpful.
Highly recommended
By Kaz
I had to learn this technique a couple of years ago and the book helped me to quickly apply the technique to several research problems. The author was also very helpful as he answered my questions by email and shared his macro codes. After understanding the method conceptually and learned to use his macro, I wrote my own routine using PROC LOGISTIC. I still use the author's code to QC my results. I like his summaries/suggestions about result verification techniques.
I actually enjoyed learning about it through this book. I bought other two books recommended for purchase with this book, but I am glad I started from this practical and well-written introduction to the method.
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