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decisioncompare Rare Disease Data

Source: R/data_decisioncompare_docs.R
decisioncompare_rare.Rd

Dataset with 300 patients and very low disease prevalence (5%), typical of rare disease screening. Tests: high sensitivity (0.90) vs high specificity (0.85/0.95).

Usage

decisioncompare_rare

Format

A data frame with 300 rows and 4 variables:

patient_id

Character: Patient identifier (PT001-PT300)

GoldStandard

Factor: True status ("Negative", "Positive"), 5% positive

Test1

Factor: High sensitivity test ("Negative", "Positive"), Sens=0.90, Spec=0.90

Test2

Factor: High specificity test ("Negative", "Positive"), Sens=0.85, Spec=0.95

screening_round

Numeric: Screening round (1-5)

Source

Generated test data for ClinicoPath package

Details

Rare disease setting (5% prevalence). Demonstrates impact of low prevalence on PPV/NPV. High specificity crucial to minimize false positives.

Examples

data(decisioncompare_rare)
decisioncompare(data = decisioncompare_rare, gold = "GoldStandard",
                goldPositive = "Positive", test1 = "Test1",
                test1Positive = "Positive", test2 = "Test2",
                test2Positive = "Positive", pp = TRUE, pprob = 0.05)
#> Error in decisioncompare(data = decisioncompare_rare, gold = "GoldStandard",     goldPositive = "Positive", test1 = "Test1", test1Positive = "Positive",     test2 = "Test2", test2Positive = "Positive", pp = TRUE, pprob = 0.05): argument "test3Positive" is missing, with no default