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Positive predictive value of screening tests

Positive predictive value of screening tests. PPV versus sensitivity: different denominators!. Sensitivity = those who screen positive/ total number with disease [a/(a+c)] PPV= those who are true positives/total positive screens [a/(a+b)]. How prevalence affects PPV.

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Positive predictive value of screening tests

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  1. Positive predictive value of screening tests

  2. PPV versus sensitivity: different denominators! Sensitivity = those who screen positive/ total number with disease [a/(a+c)] PPV= those who are true positives/total positive screens [a/(a+b)]

  3. How prevalence affects PPV This is for the sceptics- the mathematical proof follows on the next few slides… • Start with a population of 1000 people • Assume that you have a screening test with sensitivity= 90% and specificity= 90% • How will PPV be affected if the disease prevalence is 10% in one case and 1% in another?

  4. Disease prevalence 1% • Prevalence of 1% means 10 disease cases in a • population of 1000 • Sensitivity of 90% means 9 cases are detected on screen • Specificity of 90% means that 891 out of 990 healthy people are correctly identified as true negatives • PPV= 9/108= 0.08= 8% or for every 100 people who screen positive, only 8 truly have the disease

  5. Disease prevalence 10% • Prevalence of 10% means 100 disease cases in a • population of 1000 • Sensitivity of 90% means 90 cases are detected on screen • Specificity of 90% means that 810 out of 900 healthy people are correctly identified as true negatives • PPV= 90/180= 0.5= 50% or for every 100 people who screen positive, 50 will actually have the disease

  6. Ergo… Given a fixed sensitivity and specificity for a screening test, the positive predictive value increases with increasing disease prevalence. Q.E.D.

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