Dataset with 250 patients comparing screening test (high sensitivity) vs diagnostic test (high specificity) in low prevalence setting (15%).
Format
A data frame with 250 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT250)
- Biopsy
Factor: Biopsy result ("Negative", "Positive"), 15% positive
- ScreeningTest
Factor: Screening test ("Negative", "Positive"), Sens=0.95, Spec=0.80
- DiagnosticTest
Factor: Diagnostic test ("Negative", "Positive"), Sens=0.85, Spec=0.92
- age
Numeric: Patient age in years (mean 62, SD 8)
- risk_score
Numeric: Risk score (mean: 7 for positive, 3 for negative)
Details
Demonstrates trade-off between sensitivity (screening) and specificity (diagnostic). Low prevalence setting typical of screening programs.
Examples
data(decisioncompare_screening)
decisioncompare(data = decisioncompare_screening, gold = "Biopsy",
goldPositive = "Positive", test1 = "ScreeningTest",
test1Positive = "Positive", test2 = "DiagnosticTest",
test2Positive = "Positive", pp = TRUE, pprob = 0.15)
#> Error in decisioncompare(data = decisioncompare_screening, gold = "Biopsy", goldPositive = "Positive", test1 = "ScreeningTest", test1Positive = "Positive", test2 = "DiagnosticTest", test2Positive = "Positive", pp = TRUE, pprob = 0.15): argument "test3Positive" is missing, with no default