Bootstrap Youden index confidence interval: calculate a BC a bootstrapped 95% confidence interval for the Youden index and its associated criterion value.Estimation of sensitivity and specificity at fixed specificity and sensitivity: compile a table with estimation of sensitivity and specificity, with a BC a bootstrapped 95% confidence interval (Efron, 1987 Efron & Tibshirani, 1993), for a fixed and prespecified specificity and sensitivity of 80%, 90%, 95% and 97.5% (Zhou et al., 2002).Advanced: click this button for some advanced options: These options require bootstrapping and are computationally intensive and time consuming.In this case S, and the "optimal criterion value" depends only on the disease prevalence. The parameter S is "cost-neutral" when (FPc-TNc)/(FNc-TPc) evaluates to 1, that is when FPc-TNc equals FNc-TPc. When TNc is smaller than FPc then TPc must be smaller than FNc.When TNc is larger than FPc then TPc must be larger than FNc.Then for FNc you enter 2, for FPc enter 1 and enter 0 for both TPc and TNc.īecause the slope S must be a positive number: Suppose a false negative (FN) decision is judged to be twice as costly as a false positive (FP) decision, and no assumptions are made about the costs for true positive and true negative decisions. Benefits can be expressed as negative costs. The point on the ROC curve where a line with this slope S touches the curve is the optimal operating point, taking into account prevalence and the costs of the different decisions.Ĭosts can be financial costs or health costs, but all 4 cost factors need to be expressed on a common scale. $$ NPV = \frac \right ) $$ where P denotes the prevalence in the target population (Greiner et al., 2000). Sensitivity: probability that a test result will be positive when the disease is present (true positive rate, expressed as a percentage).The following statistics can be defined: Sensitivity The different fractions (TP, FP, TN, FN) are represented in the following table. On the other hand, some cases without the disease will be correctly classified as negative (TN = True Negative fraction), but some cases without the disease will be classified as positive (FP = False Positive fraction).
Indeed, the distribution of the test results will overlap, as shown in the following figure.įor every possible cut-off point or criterion value you select to discriminate between the two populations, there will be some cases with the disease correctly classified as positive (TP = True Positive fraction), but some cases with the disease will be classified negative (FN = False Negative fraction). When you consider the results of a particular test in two populations, one population with a disease, the other population without the disease, you will rarely observe a perfect separation between the two groups. ROC curves can also be used to compare the diagnostic performance of two or more laboratory or diagnostic tests (Griner et al., 1981). The diagnostic performance of a test, or the accuracy of a test to discriminate diseased cases from normal cases is evaluated using Receiver Operating Characteristic (ROC) curve analysis (Metz, 1978 Zweig & Campbell, 1993). The ROC curve is a fundamental tool for diagnostic test evaluation. MedCalc creates a complete sensitivity/specificity report. The Area Under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal).
Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A ROC curve is a plot of the true positive rate (Sensitivity) in function of the false positive rate (100-Specificity) for different cut-off points of a parameter.