The scenario
A patient with non-specific symptoms is tested for a rare disease with prevalence 0.1%. A screening test has 95% sensitivity and specificity, and the tree calculates the likelihood of each possible outcome, revealing the high risk of false positives despite the high specificity.
What is in this drawing
Read the decisions behind it.
01
Whether to test given low prior probability
02Interpretation of a positive result
low positive predictive value despite high specificity
03Interpretation of a negative result
high negative predictive value reassuring
Use this pattern to analyze diagnostic strategies and communicate the impact of prior probability on test interpretation. Also applies to any sequential probabilistic decision, such as credit scoring or quality control sampling.
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