By Jonathan Chaffer, Karl Swedberg
As a diagnostic decision-making device, receiver working attribute (ROC) curves offer a entire and visually appealing solution to summarize the accuracy of predictions. they're largely utilized in clinical prognosis and more and more in fields resembling information mining, credits scoring, climate forecasting, and psychometry. during this example-driven ebook, writer Mithat GÃ¶nen illustrates the various current SAS methods that may be adapted to provide ROC curves and expands upon extra analyses utilizing different SAS strategies and macros. either parametric and nonparametric tools for reading ROC curves are lined intimately.
themes 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 a number of markers and adjusting them for covariates
- ROC curves with censored facts
- Using the ROC curve for comparing multivariable prediction types through bootstrap and cross-validation
- ROC curves in SAS firm Miner
- And extra!
Written for any statistician attracted to studying extra approximately ROC curve method, the e-book assumes readers have a easy knowing of regression approaches 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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Extra info for Analyzing Receiver Operating Characteristic Curves With SAS
Note that AUC1 and s1 are reported when the %ROC macro is executed for the first predictor only, as explained in Chapter 3. Similarly, AUC2 and s2 are estimated from a second %ROC call. 3683. 3. 5 Comparing the Binormal ROC Curves Chapter 3 shows that you can achieve a model-based estimate of the ROC curve by assuming that the distributions of the marker for the diseased and non-diseased groups have normal distributions with possibly different parameters. Specifically, if diseased patients’ marker values follow a normal distribution with mean μ1 and variance σ12 and if non-diseased patients’ marker values follow a normal distribution with mean μ0 and variance σ02, then the ROC curve can functionally be written as Φ ( a + bΦ −1 ( x) ) with the AUC given by ⎛ a ⎞ Φ⎜ ⎟ 2 ⎝ 1+ b ⎠ where the binormal parameters a and b are given by a= μ1 − μ 0 σ , b= 0 σ1 σ1 Next, we discuss paired data in detail; comments about unpaired data appear at the end of this section.
The following macro call performs the needed comparison: %roc(data=bone, var=bsi suv1, response=gold, contrast=1 -1); The VAR macro variable specifies the names of the variables containing the two marker values. The CONTRAST macro variable expects input similar to the CONTRAST statement in PROC GLM, so 1 -1 refers to the difference of the two variables stated in the VAR macro variable. 1 shows the results of this call. The AUC estimates for each of the ROC curves, along with their standard errors and (marginal) confidence intervals, appear first.
This is equivalent to choosing a threshold, which itself is equivalent to choosing an operating point on the ROC curve. Sometimes external criteria may guide the choice of an operating point. In the absence of such criteria, you might choose a threshold that is optimal in some sense. There are two widely used ways of doing so: 1. Choose the threshold that will make the resulting binary prediction as close to a perfect predictor as possible. 2. Choose the threshold that will make the resulting binary prediction as far way from a noninformative predictor as possible.
Analyzing Receiver Operating Characteristic Curves With SAS by Jonathan Chaffer, Karl Swedberg