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How does the fictional screening model illustrate the difference between P(+|D) and P(D|+)?

The model illustrates the difference by showing that P(+∣D)P(+\mid D) (sensitivity) is fixed at 95%95\%, while P(D∣+)P(D\mid +) (positive predictive value) is calculated to be only  16.1%~16.1\%. This demonstrates that the probability of a positive test given disease is not the same as the probability of disease given a positive test, especially when prevalence is low.

Conditions

  • Sensitivity is 95%95\%.
  • Calculated P(D∣+)P(D\mid +) is  16.1%~16.1\%.
  • Prevalence is 1%1\%.

Reasoning, step by step

  1. Identify P(+∣D)P(+\mid D) as the sensitivity, given as 95%95\%.
  2. Calculate P(D∣+)P(D\mid +) using the counts: 19 true positives out of 118 total positives.
  3. Compare the two values: 95%95\% vs. 16.1%16.1\%.
  4. Explain that the discrepancy arises from the inclusion of false positives in the denominator of P(D∣+)P(D\mid +).
  5. Conclude that conditioning on the test result reverses the perspective and changes the probability significantly.

Example

The script states: "Thus P(D∣+)=19/118≈16.1%P(D\mid +)=19/118\approx16.1\%. This differs from P(+∣D)=95%P(+\mid D)=95\%."

Common misconceptions

  • Assuming P(+∣D)=P(D∣+)P(+\mid D) = P(D\mid +).
  • Thinking that sensitivity is the same as positive predictive value.
  • Ignoring the impact of false positives on the conditional probability.

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