nogoldstandard Missing Data — nogoldstandard

Dataset with 150 patients including missing values in test results (~5-8% missingness per test). Three tests with good characteristics.

Usage

nogoldstandard_missing

Format

A data frame with 150 rows and 5 variables:

patient_id: Character: Patient identifier (PT001-PT150)
Test1: Factor: First test ("Negative", "Positive"), ~7% missing, Sens=0.85, Spec=0.85
Test2: Factor: Second test ("Negative", "Positive"), ~5% missing, Sens=0.80, Spec=0.88
Test3: Factor: Third test ("Negative", "Positive"), ~8% missing, Sens=0.82, Spec=0.90
age: Numeric: Patient age in years (mean 58, SD 12)

Source

Generated test data for ClinicoPath package

Details

Simulated with 30% prevalence. Missing data introduced randomly to test listwise deletion and missing data handling.

Examples

data(nogoldstandard_missing)
nogoldstandard(data = nogoldstandard_missing,
               test1 = "Test1", test1Positive = "Positive",
               test2 = "Test2", test2Positive = "Positive",
               test3 = "Test3", test3Positive = "Positive")
#> Error in nogoldstandard(data = nogoldstandard_missing, test1 = "Test1",     test1Positive = "Positive", test2 = "Test2", test2Positive = "Positive",     test3 = "Test3", test3Positive = "Positive"): argument "test4Positive" is missing, with no default