Prevention Book

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Positive Predictive Value

Aka: Positive Predictive Value
  1. See Also
    1. Screening Test
    2. Contingency Grid or Cross Tab (includes Statistics Example)
    3. Bayes Theorem (Bayesian Statistics)
    4. Fagan Nomogram
    5. Experimental Error (Experimental Bias)
    6. Lead-Time Bias
    7. Length Bias
    8. Selection Bias (Screening Bias)
    9. Likelihood Ratio (Positive Likelihood Ratio, Negative Likelihood Ratio)
    10. Number Needed to Screen (Number Needed to Treat, Absolute Risk Reduction, Relative Risk Reduction)
    11. Negative Predictive Value
    12. Pre-Test Odds or Post-Test Odds
    13. Receiver Operating Characteristic
    14. Test Sensitivity (False Negative Rate)
    15. Test Specificity (False Positive Rate)
    16. U.S. Preventive Services Task Force Recommendations
  2. Definition
    1. Percent of patients with positive test having disease
    2. P(Disease | test positive)
    3. Assesses reliability of positive test
  3. 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
  4. Calculation
    1. PPV = (True positive) / (True positive + False positive)
  5. Example 1: High Prevalence Disease
    1. Major DepressionPrevalence 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)
  6. Example 2: Low Prevalence Disease
    1. SclerodermaPrevalence 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

Positive Predictive Value of Diagnostic Test (C1514243)

Definition (NCI_NCI-GLOSS) The likelihood that an individual with a positive test result truly has the particular gene and/or disease in question.
Definition (NCI) The probability that an individual is affected with the condition when a positive test result is observed. Predictive values should only be calculated from cohort studies or studies that legitimately reflect the number of people in the population who have the condition of interest at that time since predictive values are inherently dependent upon the prevalence. PPVDT can be determined by calculating: number of true positive results divided by the sum of true positive results plus number of false positive results.
Concepts Quantitative Concept (T081)
English PPV, positive predictive value, Positive Predictive Value of Diagnostic Test
Sources
Derived from the NIH UMLS (Unified Medical Language System)


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