M3 · Diagnostic tests

Diagnostic test performance

Enter a test's results and read sensitivity, specificity and predictive values — then move prevalence and watch the predictive values change.

Disease +
Disease −
Test +
Test −

Sensitivity

—

95% CI —

Specificity

—

95% CI —

PPV

—

95% CI —

NPV

—

95% CI —

LR+

—

LR−

—

Accuracy

—

PPV and NPV move with the prevalence slider below; Sensitivity, Specificity, LR+, LR− and Accuracy are fixed properties of the test.

Disease prevalence in the population—

Set to your sample's prevalence — drag to ask what happens in another population.

Enter the test results to see performance.

Confusion matrix

Disease +Disease −Test +Test −–True positive–False positive–False negative–True negative

PPV / NPV vs prevalence

0%25%50%75%100%0%25%50%75%100%Prevalence →PPVNPV

Sensitivity and specificity stay fixed — only the predictive values move with prevalence.

ROC space Youden's J = —

Ideal0101False positive rate (1 − Sp)True positive rate (Se)
Build a full ROC curve
ThresholdSensitivity (%)Specificity (%)
Threshold 1
Threshold 2
Threshold 3
Threshold 4
Threshold 5
Threshold 6
Threshold 7

Add at least two thresholds to draw a curve.

How it's computed

Inputs TP=– FP=– FN=– TN=–  N=–
Se = TP/(TP+FN) = –/– = –
Sp = TN/(TN+FP) = –/– = –
PPV = TP/(TP+FP) = –/– = – (at the sample's own prevalence)
LR+ = Se/(1−Sp) = –/– = –
Bayes PPV at prevalence –:
= Se·p / (Se·p + (1−Sp)·(1−p)) = (–·–) / ((–·–) + (–·–)) = –
Show confidence-interval maths (Wilson)
Se: centre = (TP + 1.96²/2)/((TP+FN) + 1.96²) ± halfwidth → [–, –]
Sp: centre = (TN + 1.96²/2)/((TN+FP) + 1.96²) ± halfwidth → [–, –]
PPV: centre = (TP + 1.96²/2)/((TP+FP) + 1.96²) ± halfwidth → [–, –]
NPV: centre = (TN + 1.96²/2)/((TN+FN) + 1.96²) ± halfwidth → [–, –]