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Plot versus prevalence

Next selectROC curves
Next selectPlot versus prevalence


In this graph (part of ROC curve analysis) you can plot positive and negative predictive values against disease prevalence, for one pair of sensitivity/specificity values.

You obtain sensitivity and specificity values from ROC curve analysis or from the literature.

Optionally the program can draw the 95% confidence intervals.

Required input

Giguère et al. (2016) studied the use of stall-side serum amyloid (SAA) for early detection of pneumonia on a farm endemic for R. equi. For an optimal cut-point of >53 μg/mL they found a sensitivity of 64% and a specificity of 77%.

Plot versus prevalence


In the graph the positive and negative predictive values are plotted against disease prevalence.

Plot versus prevalence

Plotting the 95% confidence intervals

MedCalc can also draw the 95% confidence intervals in the graph. This requires you to enter the number of cases in the diseased and normal groups. These are the number of cases included in the study in which sensitivity and specificity were established.

First click the option 95% Confidence Interval and next enter the number of cases in the diseased and normal groups.

The study by Giguère et al. (2016) included 25 foals with a culture-confirmed R. equi pneumonia (the positive or diseased cases in which sensitivity was established), and 22 foals that remained clinically healthy during the entire breeding season (the negative or normal cases, in which specificity was established).

Plot versus prevalence


The new graph includes the 95% confidence intervals for positive and negative predictive values.

Plot versus prevalence

MedCalc calculates the confidence intervals for the predictive values using the standard logit method given by Mercaldo et al. 2007.


See also