Predict values in sas jmp
This chapter provides an overview and introduction to ROC for the purposes of comparing multiple test results in order select the most beneficial. 2001, Cambridge, UK: Cambridge University Press. Hunink MGM, Glasziou PP, Siegel JE, Weeks JC, Pliskin J, Elstein A & Weinstein M. Decision Making in Health and Medicine: Integrating Evidence and Values, Chapter 7.
#Predict values in sas jmp code#
Since SAS did not have a built-in ROC procedure, this book provides the necessary macros and code and links to datasets available online. It has a fair amount of theory/ background but this is not its primary goal or strength. 2007, Cary: North Carolina: SAS Publishing.Īs with most SAS-specific books, this is a very practical guide. Gönen M. Analyzing Receiver Operating Characteristic Curves with SAS. Negative predictive value: probability that the disease is not present when the test is negative (expressed as a percentage). Positive predictive value: probability that the disease is present when the test is positive (expressed as a percentage). Negative likelihood ratio: ratio between the probability of a negative test result given thepresence of the disease and the probability of a negative test result given the absence of the disease, i.e.= False negative rate / True negative rate = (1-Sensitivity) / Specificity Positive likelihood ratio: ratio between the probability of a positive test result given thepresence of the disease and the probability of a positive test result given the absence of the disease, i.e.= True positive rate / False positive rate = Sensitivity / (1-Specificity) Specificity: probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage). Sensitivity: probability that a test result will be positive when the disease is present (true positive rate, expressed as a percentage). TN= True Negative: cases without the disease correctly classified as negativeįP= False Positive: cases without the disease incorrectly classified as positive TP=True Positive: cases with the disease correctly classified as positiveįN= False Negative: cases with the disease incorrectly classified as negative The different fractions (TP, FP, TN, FN) are represented in the following table. TO understand ROC curves, it is helpful to get a grasp of sensitivity, specificity, positive preditive value and negative predictive value: The closer the curve is to the 45-degree diagonal, the less accurate the test. The closer the curve follows the left side border and the top border, the more accurate the test. Each point on the ROC curve represents a sensitivity/specificity pair.
ROC Curves plot the true positive rate (sensitivity) against the false positive rate (1-specificity) for the different possible cutpoints of a diagnostic test. ROC curves can also be used to compare the diagnostic performance of two or more laboratory tests. The diagnostic performance of a test is the accuracy of a test to discriminate diseased cases from normal controls.
Estimates of the area under the curve (AUC) provide an indication of the utility of the predictor and a means of comparing (testing) two or more predictive models. This illustrates the merit of the particular predictor/predictive model, making it possible to identify different cut-points for specific applications – depending on the ‘cost’ of misclassification. Receiver Operating Characteristic (ROC) Curves provide a graphical representation of the range of possible cut points with their associated sensitivity vs. These are useful tools, but have the disadvantage of referencing a single cut-point, and requiring an abstract assessment of the appropriate trade-off between sensitivity and specificity, while PPV and NPV are influenced by population prevalence. non-diseased), we typically consider sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). When evaluating the performance of a screening test, an algorithm or a statistical model – such as a logistic regression – for which the outcome is dichotomous (e.g. This page briefly describes methods to evaluate risk prediction models using ROC curves.