Test datasets for evaluating diagnostic test performance using the decision function. These datasets cover various clinical scenarios including screening tests, diagnostic tests, biomarkers, imaging, and pathology evaluations with different prevalence rates and test characteristics.
Usage
decision_test
decision_screening
decision_diagnostic
decision_biomarker
decision_imaging
decision_infectious
decision_small
decision_large
decision_perfect
decision_poor
decision_rare
decision_common
decision_missing
decision_multilevel
decision_pathology
decision_pointofcareFormat
Data frames with variables for gold standard reference and test results:
- patient_id
Patient identifier
- GoldStandard/Reference
True disease status (gold standard)
- NewTest/Test
Test result being evaluated
- Additional clinical variables
Age, symptoms, risk factors, etc.
decision_test: Basic diagnostic test evaluation (n=200, 30% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive, Sens=0.85, Spec=0.90)
- age
Patient age in years
- sex
Patient sex (Female/Male)
decision_screening: Cancer screening test with low prevalence (n=250, 5% prevalence)
- patient_id
Patient identifier
- Biopsy
Gold standard biopsy result (Benign/Malignant)
- ScreeningTest
Screening test result (Negative/Positive, Sens=0.92, Spec=0.88)
- age
Patient age in years
- risk_factor
Cancer risk factor (Low/Medium/High)
decision_diagnostic: Clinical diagnostic test with high prevalence (n=180, 60% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Absent/Present)
- ClinicalTest
Clinical test result (Negative/Positive, Sens=0.88, Spec=0.85)
- symptom_severity
Symptom severity score (1-10)
- duration_days
Symptom duration in days
decision_biomarker: Cardiac biomarker test evaluation (n=220, 35% prevalence)
- patient_id
Patient identifier
- Angiography
Gold standard angiography (No_MI/MI)
- Troponin
Troponin test result (Normal/Elevated, Sens=0.95, Spec=0.92)
- chest_pain
Chest pain severity (None/Mild/Severe)
- ecg_changes
ECG findings (Normal/Abnormal)
decision_imaging: Medical imaging test performance (n=160, 40% prevalence)
- patient_id
Patient identifier
- Pathology
Pathology diagnosis (Benign/Malignant)
- CT_Scan
CT scan result (Normal/Abnormal, Sens=0.87, Spec=0.83)
- tumor_size_cm
Tumor size in centimeters
- location
Tumor location (Lung/Liver/Kidney/Pancreas)
decision_infectious: Infectious disease rapid test (n=200, 25% prevalence)
- patient_id
Patient identifier
- Culture
Gold standard culture result (Negative/Positive)
- RapidTest
Rapid test result (Negative/Positive, Sens=0.82, Spec=0.95)
- fever
Fever present (No/Yes)
- symptom_onset_days
Days since symptom onset
decision_small: Small sample dataset (n=30, 40% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive, Sens=0.85, Spec=0.85)
decision_large: Large sample dataset (n=500, 30% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive, Sens=0.88, Spec=0.90)
- age
Patient age in years
- comorbidities
Number of comorbidities
decision_perfect: Perfect test performance (n=150, 35% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- PerfectTest
Perfect test result (Sens=1.00, Spec=1.00)
- age
Patient age in years
decision_poor: Poor test performance (n=140, 30% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- PoorTest
Poor performing test (Sens=0.60, Spec=0.65)
decision_rare: Rare disease scenario (n=300, 2% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive, Sens=0.90, Spec=0.95)
- genetic_marker
Genetic marker status (Absent/Present)
decision_common: Common disease scenario (n=180, 75% prevalence)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive, Sens=0.85, Spec=0.88)
decision_missing: Dataset with missing values (n=150, ~5% missing)
- patient_id
Patient identifier
- GoldStandard
True disease status with missing values
- NewTest
Test result with missing values
- age
Patient age in years
decision_multilevel: Three-level variables with indeterminate results (n=180)
- patient_id
Patient identifier
- GoldStandard
True disease status (Negative/Positive)
- NewTest
Test result (Negative/Positive/Indeterminate, ~10% indeterminate)
decision_pathology: Pathology frozen section evaluation (n=190, 45% prevalence)
- patient_id
Patient identifier
- Biopsy
Permanent section diagnosis (Benign/Malignant)
- FrozenSection
Frozen section diagnosis (Benign/Malignant, Sens=0.93, Spec=0.96)
- specimen_type
Specimen type (Core_Biopsy/Excision/FNA)
- tumor_grade
Tumor grade (Low/Intermediate/High)
decision_pointofcare: Point-of-care test evaluation (n=210, 28% prevalence)
- patient_id
Patient identifier
- LabTest
Laboratory test result (Negative/Positive)
- PointOfCare
Point-of-care test result (Negative/Positive, Sens=0.78, Spec=0.92)
- setting
Testing setting (Emergency/Clinic/Home)
- urgency
Clinical urgency (Routine/Urgent/Critical)
Details
All datasets use realistic clinical scenarios with appropriate sensitivity, specificity, and disease prevalence values. Datasets include edge cases (small/large samples, perfect/poor performance, rare/common diseases) for comprehensive testing.
Examples
# Load basic test dataset
data(decision_test)
head(decision_test)
#> # A tibble: 6 × 5
#> patient_id GoldStandard NewTest age sex
#> <chr> <fct> <fct> <dbl> <fct>
#> 1 PT001 Positive Negative 31 Male
#> 2 PT002 Positive Positive 59 Male
#> 3 PT003 Negative Negative 69 Male
#> 4 PT004 Positive Negative 80 Female
#> 5 PT005 Negative Negative 38 Male
#> 6 PT006 Negative Negative 41 Male
# Evaluate diagnostic test
decision(
data = decision_test,
gold = "GoldStandard",
goldPositive = "Positive",
newtest = "NewTest",
testPositive = "Positive"
)
#> Error in decision(data = decision_test, gold = "GoldStandard", goldPositive = "Positive", newtest = "NewTest", testPositive = "Positive"): argument "goldNegative" is missing, with no default