Epi

Positive Predictive Value

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Positive Predictive Value

  • Definition
  1. Percent of patients with positive test having disease
  2. P(Disease | test positive)
  3. Assesses reliability of positive test
  • Indications
  1. Puts Test Specificity in context of disease Prevalence
  2. Lower disease Prevalence results in lower PPV
    1. Test Specificity effect is magnified
    2. False positives increase substantially
    3. Results in less reliable positive test
    4. Example: HIV Test in a patient in a low risk, low Prevalence cohort has an increased risk of False Positive testing
  • Calculation
  1. PPV = (True positive) / (True positive + False positive)
  1. Major Depression Prevalence is 10 per 100
  2. New Screening Test efficacy
    1. Test Sensitivity: 100%
    2. Test Specificity: 99% (1 false positive in 100)
  3. Screen 1000 patients
    1. True positives: 100 per 1000 (10% Prevalence)
    2. False positives: 10 per 1000 (99% Test Specificity)
    3. PPV: 100 true positives / 110 total positives = 91%
  4. Summary
    1. Pre-Test Probability: 10% (baseline Prevalence)
    2. Post-Test Probability: 91% (PPV)
  1. Scleroderma Prevalence is 1 per 1000
  2. New Screening Test efficacy
    1. Test Sensitivity: 100%
    2. Test Specificity: 99% (1 false positive in 100)
  3. Screen 1000 patients
    1. True positives: 1 per 1000 (0.1% Prevalence)
    2. False positives: 10 per 1000 (99% Test Specificity)
    3. PPV: 1 true positive / 11 total positives = 9%
  4. Summary
    1. Pre-Test Probability: 0.1% (baseline Prevalence)
    2. Post-Test Probability: 9% (PPV)
      1. Contrast with PPV 91% for high Prevalence disease