1 / 6

Understanding Positive Predictive Value in Screening Tests: A Mathematical Analysis

Explore how disease prevalence impacts PPV using sensitivity and specificity metrics. Learn through population scenarios with different prevalences.

coucha
Download Presentation

Understanding Positive Predictive Value in Screening Tests: A Mathematical Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related