By Jonathan Chaffer, Karl Swedberg
As a diagnostic decision-making device, receiver working attribute (ROC) curves supply a complete and visually beautiful approach to summarize the accuracy of predictions. they're generally utilized in clinical prognosis and more and more in fields akin to information mining, credits scoring, climate forecasting, and psychometry. during this example-driven e-book, writer Mithat GÃ¶nen illustrates the numerous current SAS tactics that may be adapted to supply ROC curves and expands upon additional analyses utilizing different SAS techniques and macros. either parametric and nonparametric tools for reading ROC curves are coated intimately.
themes addressed comprise:
- Appropriate equipment for binary, ordinal, and non-stop measures
- Computations utilizing PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros
- Comparing the ROC curves of numerous markers and adjusting them for covariates
- ROC curves with censored information
- Using the ROC curve for comparing multivariable prediction versions through bootstrap and cross-validation
- ROC curves in SAS firm Miner
- And extra!
Written for any statistician attracted to studying extra approximately ROC curve technique, the publication assumes readers have a simple knowing of regression strategies and reasonable familiarity with Base SAS and SAS/STAT. a few familiarity with SAS/GRAPH is useful yet now not crucial.
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Additional info for Analyzing Receiver Operating Characteristic Curves With SAS
7 Bootstrap Confidence Intervals for the Difference in the Area under the Empirical ROC Curve..................................................................... 8 Covariate Adjustment for ROC Curves ................................................. 9 Regression Model for the Binormal ROC Curve .................................... 1 Introduction In many instances there will be a set of competing predictions to choose from and hence it is often of interest to compare predictions. For example, how does an expensive invasive procedure or radiological scan compare with a simple clinical exam to diagnose a medical condition?
Unfortunately, the two criteria of optimality produce different optimal operating points. This is analytically demonstrated by Perkins and Schisterman (2006). Most of the time, the two are close enough to allay any concerns about the choice of method. 6 The Binormal ROC Curve So far our focus has been on the empirical curve, the one obtained by connecting the observed (sensitivity, 1-specificity) pairs. The empirical curve is widely used because it is easy to produce and it is robust (in the sense of being rank-based) to the changes in the marker distribution.
02727. 934). 3 compares the results from the binormal model with those from the empirical analyses. Both the AUC estimates and the confidence intervals are very close to each other. 934 From the standpoint of ROC curve analyses, the ESTIMATE statement is an important strength of PROC NLMIXED because it allows you to directly obtain estimates and, perhaps more importantly, standard errors for a, b, and AUC. Another advantage that will be apparent in subsequent chapters is that it is flexible enough to accommodate several different types of ROC curves.