Diagnostic test evaluation and comparison
# Calculate test Sensitivity and Specificity and ROC curves

This utility calculates test sensitivity and specificity for a test producing a continuous outcome. Suggested cut-points are calculated for a range of target values for sensitivity and specificity. A ROC curve and two-grah ROC curve are generated and Youden's index (*J*
and test efficiency (for selected prevalence values (are also calculated).

This utility calculates test sensitivity and specificity for a test producing a continuous outcome. Suggested cut-points are calculated for a range of target values for sensitivity and specificity. A ROC curve and two-grah ROC curve are generated and Youden's index (

Inputs:

- the desired level of confidence in the resulting sensitivity and specificity estimates; and
- two columns of data for analysis. Data required is a series of test results for both infected and uninfected individuals. This data can be pasted in either of two formats:
- stacked - the first column contains status identifiers as either "Infected" or "Uninfected" and the second column contains the corresponding test result; or
- unstacked - separate columns contain test results for infected and uninfected individuals. Column order is unimportant but columns must be labelled appropriately as "Infected" or "Uninfected" in a header row;

Outputs:

- numerical and graphical summaries of testing results for both infected and uninfected groups;
- cut-point values to achieve minimum target values for both sensitivity and specificity along with corresponding estimates and Wilson binomial confidence intervals;
- one and two-graph ROC curves, with estimated AUC for the one-graph curve;
- area under the ROC curve (AUC) and associated DeLong confidence limits and Z test. See DeLong et al. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
*Biometrics***44**:837-845; - graphs of Youden's J Index and test efficiency for a range of prevalence values;
- graphs of mis-classification cost terms for a range of prevalence values and relative costs of false negative/false positive; and
- detailed sensitivity and specificity results in a downloadable spreadsheet file.

For more information, see Greiner, M, Pfeiffer, D and Smith, RD (2000). Principles and practical application of the receiver-operating characteristic analysis for diagmostic tests. *Preventive Veterinary Medicine* **45**:23-41.