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A Macro For Getting More Out Of Your ROC Curve
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lambert, Jennifer L. Lipkovich, Ilya |
| Copyright Year | 2008 |
| Abstract | As a part of clinical decision making, a continuous measure reflective of a patient’s condition (e.g. laboratory test result) is often dichotomized (or “cut”) into two groups (e.g., abnormal and normal) for predictive or screening purposes. The decision of where to select the cut-off point is often governed by a reasonable trade-off between sensitivity and specificity for a particular test. A common method to help find this balance is to plot sensitivity versus (1-specificity) as a "ROC” (Receiver Operator Characteristic) curve. Although a useful tool, the ROC curve rarely displays the individual cut-off values. Because of this limitation, the user cannot visualize the impact of varying sensitivity and specificity against the cut-off values of the prediction variable. The macro presented in this paper creates a single graph that, for a given prediction variable and a binary outcome (“true condition”), simultaneously displays the following: sensitivity, specificity, Youden Index, and various userdefined measures of misclassification error against cut-off values of the prediction variable. The graph is a simple but highly informative visual tool that provides the user with greater functionality than a standard ROC curve. This macro is intended for an intermediate SAS user with PC SAS (SAS/STAT® and SAS/GRAPH®) version 8.2 or 9.1 capabilities. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www2.sas.com/proceedings/forum2008/231-2008.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
