Book description
As a diagnostic decisionmaking 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 Gonen 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 crossvalidation
 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.
This book is part of the SAS Press program.
Table of contents
 Cover Page
 Title Page
 Copyright Page
 Contents
 Preface
 Acknowledgments
 Chapter 1  Introduction
 Chapter 2  Single Binary Predictor

Chapter 3  Single Continuous Predictor
 3.1  Dichotomizing a Continuous Predictor
 3.2  The ROC Curve
 3.3  Empirical ROC Curve and the Conditional Distributions of the Marker
 3.4  Area under the ROC Curve
 3.5  Selecting an Optimal Threshold
 3.6  The Binormal ROC Curve
 3.7  Transformations to Binormality
 3.8  Direct Estimation of the Binormal ROC Curve
 3.9  Bootstrap Confidence Intervals for the Area Under the Curve

Chapter 4  Comparison and Covariate Adjustment of ROC Curves
 4.1  Introduction
 4.2  An Example from Prostate Cancer Prognosis
 4.3  Paired Versus Unpaired Comparisons
 4.4  Comparing the Areas Under the Empirical ROC Curves
 4.5  Comparing the Binormal ROC Curves
 4.6  Discrepancy Between Binormal and Empirical ROC Curves
 4.7  Bootstrap Confidence Intervals for the Difference in the Area Under the Empirical ROC Curve
 4.8  Covariate Adjustment for ROC Curves
 4.9  Regression Model for the Binormal ROC Curve
 Chapter 5  Ordinal Predictors

Chapter 6  Lehmann Family of ROC Curves
 6.1  Introduction
 6.2  Lehmann Family of Distributions
 6.3  Magnetic Resonance Example
 6.4  Adjusting for Covariates
 6.5  Using Estimating Equations to Handle Clustered Data
 6.6  Comparing Markers Using the Lehmann Family of ROC Curves
 6.7  Advantages and Disadvantages of the Lehmann Family of ROC Curves
 Chapter 7  ROC Curves with Censored Data
 Chapter 8  Using the ROC Curve to Evaluate Multivariable Prediction Models
 Chapter 9  ROC Curves in SAS Enterprise Miner
 Appendix An Introduction to PROC NLMIXED
 References
 Index
 Accelerate Your SAS Knowledge with SAS Books
Product information
 Title: Analyzing Receiver Operating Characteristic Curves with SAS
 Author(s):
 Release date: April 2015
 Publisher(s): SAS Institute
 ISBN: 9781629597966
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