By Jonathan Chaffer, Karl Swedberg
As a diagnostic decision-making device, receiver working attribute (ROC) curves offer a finished and visually beautiful strategy to summarize the accuracy of predictions. they're greatly utilized in clinical prognosis and more and more in fields similar to facts mining, credits scoring, climate forecasting, and psychometry. during this example-driven ebook, writer Mithat GÃ¶nen illustrates the numerous latest SAS approaches that may be adapted to provide ROC curves and expands upon additional analyses utilizing different SAS strategies and macros. either parametric and nonparametric tools for studying ROC curves are lined intimately.
themes addressed contain:
- 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 firm Miner
- And extra!
Written for any statistician attracted to studying extra approximately ROC curve method, the ebook assumes readers have a uncomplicated realizing of regression methods and average familiarity with Base SAS and SAS/STAT. a few familiarity with SAS/GRAPH is beneficial yet no longer crucial.
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Additional resources for Analyzing Receiver Operating Characteristic Curves With SAS
Although the steps for doing so aren’t detailed here, you might want to try this exercise to gain practical skills. 6 describes how to derive the functional form of a binormal ROC curve, starting with the basic assumption that the distribution of the marker is normal (with possibly different means and variances) in positive and negative groups. It then shows how PROC NLMIXED can be used to obtain estimates of the parameters of these two normal distributions. This method assumes that the observed data are normally distributed.
This is generally true for procedures that compare the means of several groups (such as the t-test or the F-test), primarily because the sample means can be approximated by a normal distribution in large samples even if the individual observations are markedly non-normal. In contrast, the ROC curve does not simply compare the means of diseased and non-diseased populations. ) is the cumulative density in the non-diseased group. Therefore, accurate estimation of the ROC curve requires accurate estimation of all points on F and G.
The distance between a point on the ROC curve and these reference points is measured using the so-called Euclidean method. 5 with two candidate optimal operating points, A and B. R denotes the reference point for the first method of choosing an optimal threshold. Similarly, P and Q denote the reference points for the second method. According to the first definition, you need to compute the length of line segments RA and RB and prefer A over B if RA is shorter than RB. Similarly, if the second method of choosing a threshold is adapted, then the lengths of AP and BQ need to be compared, with A preferred over B if AP is longer than BQ.