By Jonathan Chaffer, Karl Swedberg
As a diagnostic decision-making instrument, receiver working attribute (ROC) curves offer a complete and visually appealing option 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 ebook, writer Mithat GÃ¶nen illustrates the various present SAS methods that may be adapted to provide ROC curves and expands upon extra analyses utilizing different SAS tactics and macros. either parametric and nonparametric tools for interpreting ROC curves are coated intimately.
issues 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 technique, the ebook assumes readers have a easy figuring out of regression systems and reasonable familiarity with Base SAS and SAS/STAT. a few familiarity with SAS/GRAPH is useful yet no longer crucial.
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Additional resources for Analyzing Receiver Operating Characteristic Curves With SAS
For example, the standard deviation of the B numbers R(i) is approximately the standard error of R. Further review of the bootstrap method is beyond the scope of this book. Consult Efron and Tibshirani (1993) for more information. com/gonen, performs a bootstrap analysis for the area under the curve of a single ROC curve. An example call is as follows: %boot1auc(data=HeadNeck,marker=suv,gold=ngold); although there are two other possible inputs: BOOT specifies the number of bootstrap samples (default of 1,000) and ALPHA specifies the error level for the confidence intervals.
1 Comparisons Based on the Binormal Model with Paired Data The paired nature of the data requires some adjustment to the model before you can proceed. Let Xij denote the marker value for the ith patient and the jth marker. Then Xij (or some transformation of it) is assumed to have the following normal distribution if the patient has a positive outcome: X ij ~ N ( μ1 j + ui , σ 12j ) Chapter 4: Comparison and Covariate Adjustment of ROC Curves 43 If the patient has a negative outcome, then Xij would be distributed as X ij ~ N ( μ0 j + ui , σ 02 j ) The ui is normally distributed as well: ui ~ N ( 0, σ r2 ) with the key assumption that ui and ui’ are independent for i≠i’.
Note that two binormal ROC curves are identical only if they have the same binormal parameters. Therefore, comparing a and b for two or more binormal curves offers one way of testing whether the two curves are identical. This is accomplished by the following CONTRAST statement: contrast 'Equality of ROC curves' a1-a0, b1-b0; which tests the null hypothesis H 0 : a0 = a1 & b0 = b1 against the alternative that at least one of the binormal parameters is different. Note that it is not possible to express this using an ESTIMATE statement.
Analyzing Receiver Operating Characteristic Curves With SAS by Jonathan Chaffer, Karl Swedberg