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tooluniverse-diagnostic-test-evaluation
Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
skills/tooluniverse-diagnostic-test-evaluation/SKILL.md
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# Diagnostic Test / Biomarker Accuracy Evaluation
Judge how well a test or biomarker discriminates disease — at a fixed cutoff (2×2) or across all cutoffs (ROC) — and turn a result into a probability of disease.
## Which case are you in?
| You have… | Go to |
|---|---|
| A 2×2 table (TP/FP/TN/FN) at a fixed cutoff | **Step 1** (`Epidemiology_diagnostic`) |
| A **continuous** biomarker score + true labels | **Step 2** (ROC / AUC / Youden, Python) |
| A test's sens/spec + a patient's pre-test probability | **Step 3** (`Epidemiology_bayesian`) |
## Step 1 — Fixed-cutoff metrics from a 2×2 table
