Dataset with 300 patients and very low disease prevalence (5%), representing rare disease screening scenarios where prevalence affects predictive values.
Format
A data frame with 300 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT300)
- rare_disease
Factor: "Disease" or "No_Disease" (5%/95% prevalence)
- biomarker
Numeric: Biomarker value (mean: 80 for disease, 45 for no disease)
Details
Low prevalence (5%) with good biomarker discrimination. Demonstrates impact of prevalence on positive and negative predictive values even with good sensitivity and specificity.
Examples
data(psychopdaROC_rare)
psychopdaROC(data = psychopdaROC_rare, dependentVars = "biomarker",
classVar = "rare_disease", positiveClass = "Disease",
refVar = "biomarker")
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> ADVANCED ROC ANALYSIS
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#> Procedure Notes
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#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
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#> Measure Variable(s): biomarker
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#> Class Variable: rare_disease
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#> Positive Class: Disease
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#> Method: maximize_metric
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#> All Observed Cutpoints: FALSE
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#> Metric: youden
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#> Direction (relative to cutpoint): >=
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#> Tie Breakers: mean
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#> Metric Tolerance: 0.05
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#>
#> <hr />
#>
#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
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#> Seed: 123Positive Class: Disease (Prevalence: 4.3%)Analysis Mode:
#> Basic<div style='background-color: #fff3cd; color: #856404; padding:
#> 10px; border-radius: 4px; margin-top: 10px;'>Warnings:Class imbalance
#> detected (Prevalence: 4.3%). Consider using Precision-Recall curves.
#>
#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> biomarker 0.9790941 0.9506579 1.0000000 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> biomarker Excellent Suitable for clinical use with appropriate cutpoint The test 'biomarker' has an AUC of 0.979 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 66.6279881 92.30769 93.03136 37.50000 99.62687 0.8533905 0.9790941 0.8533905
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> biomarker 0.9790941 0.9506579 1.0000000 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
