By Jonathan Chaffer, Karl Swedberg
As a diagnostic decision-making device, receiver working attribute (ROC) curves offer a entire and visually beautiful strategy to summarize the accuracy of predictions. they're widely utilized in scientific prognosis and more and more in fields resembling info mining, credits scoring, climate forecasting, and psychometry. during this example-driven ebook, writer Mithat GÃ¶nen illustrates the numerous latest SAS tactics that may be adapted to supply ROC curves and expands upon additional analyses utilizing different SAS tactics and macros. either parametric and nonparametric tools for examining ROC curves are coated intimately.
issues addressed comprise:
- Appropriate tools 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 info
- Using the ROC curve for comparing multivariable prediction versions through bootstrap and cross-validation
- ROC curves in SAS company Miner
- And extra!
Written for any statistician attracted to studying extra approximately ROC curve technique, the ebook assumes readers have a uncomplicated realizing of regression strategies and reasonable familiarity with Base SAS and SAS/STAT. a few familiarity with SAS/GRAPH is beneficial yet now not crucial.
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Extra resources for Analyzing Receiver Operating Characteristic Curves With SAS
5). Somers’ D is simply a rescaled version of the AUC (or concordance) that takes values between -1 and 1, like a usual correlation coefficient, instead of 0 and 1. PROC LOGISTIC does not report the standard error for Somers’ D. However, PROC FREQ reports both Somers’ D and its standard error, as in the following example: proc freq data=HeadNeck; table suv*gold_standard / noprint measures; run; The NOPRINT option suppresses the printing of the frequency table, which could be several pages long since each unique value of the SUV will be interpreted as a row of the table.
The major drawback to using PROC LOGISTIC to estimate the AUC is that the associated standard error is not available. Fortunately, PROC FREQ provides an indirect alternative for this purpose. 2 Somers’ D and AUC Using PROC FREQ PROC LOGISTIC output includes Somers’ D as well. 5). Somers’ D is simply a rescaled version of the AUC (or concordance) that takes values between -1 and 1, like a usual correlation coefficient, instead of 0 and 1. PROC LOGISTIC does not report the standard error for Somers’ D.
In contrast, the Box-Cox family attempts to normalize the marginal Chapter 3: Single Continuous Predictor 31 distribution of the marker values. You can redefine the family so that it normalizes the conditional distributions. The easiest way of doing so is recognizing that an analysis of variance model with the marker as the outcome and the gold standard as the covariate is one way of modeling the desired conditional distributions. For more information, see Sakia (1992). In the PET example, you can estimate λ using the following call to PROC TRANSREG.