psychopdaROC Test Data - Basic Diagnostic Test
Source:R/data_psychopdaROC_docs.R
psychopdaROC_test.RdBasic diagnostic test dataset with 200 patients for ROC analysis. Contains binary disease status and a continuous biomarker with moderate discrimination (AUC ~0.75-0.80).
Format
A data frame with 200 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT200)
- disease_status
Factor: "Disease" or "Healthy" (30%/70% prevalence)
- biomarker
Numeric: Continuous biomarker value (mean: 75 for diseased, 50 for healthy)
- age
Numeric: Patient age in years (mean 60, SD 12)
- sex
Factor: "Male" or "Female"
Details
Biomarker values follow normal distributions with clear separation between disease groups, suitable for demonstrating basic ROC curve analysis and optimal cutpoint determination using Youden index or other metrics.
Examples
data(psychopdaROC_test)
psychopdaROC(data = psychopdaROC_test, dependentVars = "biomarker",
classVar = "disease_status", 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: disease_status
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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 />
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#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
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#> Analysis Status
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#> Seed: 123Positive Class: Disease (Prevalence: 35.5%)Analysis Mode:
#> Basic
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#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> biomarker 0.8998799 0.8585411 0.9412187 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
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#> Clinical Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> biomarker Good Suitable for clinical use with appropriate cutpoint The test 'biomarker' has an AUC of 0.900 indicating good discriminatory ability. This test performs well for clinical decision making.
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 58.0546364 84.50704 75.96899 65.93407 89.90826 0.6047603 0.8998799 0.6047603
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> biomarker 0.8998799 0.8585411 0.9412187 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
