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Analyzing Receiver Operating Characteristic Curves With SAS (Sas Press Series)

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Analyzing Receiver Operating Characteristic Curves With SAS (Sas Press Series), 9781599942988 (1599942984), SAS Institute, 2007
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 extensively used in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In this example-driven book, author Mithat Gönen 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.

About the Author

Mithat Gönen 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.

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