Which statement about posttest probability is accurate?

Which statement about posttest probability is accurate?

• A. It equals the disease prevalence.
• B. It is determined only by the pretest probability.
• C. It is the probability after applying the test result, but ignores test accuracy.
• D. It is the probability after the test result is applied, incorporating the test's accuracy.

Answer: (D)

Posttest probability is the probability that the patient has the disease after you’ve applied the test result. It reflects both where you started (the pretest probability) and how accurate the test is (its sensitivity and specificity, usually incorporated through likelihood ratios). In practice, a positive result can raise your estimate of having the disease, while a negative result can lower it, but the change depends on the test’s performance. For example, if the pretest probability is 30% and the test has good accuracy (say, sensitivity 90% and specificity 85%), the positive result can move the probability up substantially. Using the positive likelihood ratio (LR+ = sensitivity/(1 − specificity) = 0.90/0.15 = 6), the posttest odds become 0.30/0.70 × 6 ≈ 2.57, which corresponds to a posttest probability of about 72%. This illustrates how the posttest probability explicitly incorporates the test’s accuracy. If the result were negative, you’d use the negative likelihood ratio (LR−), and the posttest probability would adjust downward accordingly.

Posttest probability is the probability that the patient has the disease after you’ve applied the test result. It reflects both where you started (the pretest probability) and how accurate the test is (its sensitivity and specificity, usually incorporated through likelihood ratios). In practice, a positive result can raise your estimate of having the disease, while a negative result can lower it, but the change depends on the test’s performance.

For example, if the pretest probability is 30% and the test has good accuracy (say, sensitivity 90% and specificity 85%), the positive result can move the probability up substantially. Using the positive likelihood ratio (LR+ = sensitivity/(1 − specificity) = 0.90/0.15 = 6), the posttest odds become 0.30/0.70 × 6 ≈ 2.57, which corresponds to a posttest probability of about 72%. This illustrates how the posttest probability explicitly incorporates the test’s accuracy.

If the result were negative, you’d use the negative likelihood ratio (LR−), and the posttest probability would adjust downward accordingly.