Dataset with 170 patients across a disease severity spectrum (Mild, Moderate, Severe) collapsed into binary classification for ROC analysis.
Format
A data frame with 170 rows and 4 variables:
- patient_id
Character: Patient identifier (PT001-PT170)
- severity
Factor: "Mild", "Moderate", or "Severe"
- binary_status
Factor: "Negative" (Mild) or "Positive" (Moderate/Severe)
- continuous_marker
Numeric: Marker increasing with severity (40/60/85)
Details
Represents continuous disease spectrum with graded marker values: Mild (mean 40), Moderate (mean 60), Severe (mean 85). Binary classification treats Mild as Negative, Moderate/Severe as Positive.
Examples
data(psychopdaROC_spectrum)
psychopdaROC(data = psychopdaROC_spectrum, dependentVars = "continuous_marker",
classVar = "binary_status", positiveClass = "Positive",
refVar = "continuous_marker")
#> Multiple optimal cutpoints found, applying break_ties.
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#> 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): continuous_marker
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#> Class Variable: binary_status
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#> Positive Class: Positive
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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: Positive (Prevalence: 67.6%)Analysis Mode:
#> Basic
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#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────────────
#> continuous_marker 0.9549407 0.9258927 0.9839887 < .0000001
#> ────────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
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#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> continuous_marker Excellent Suitable for clinical use with appropriate cutpoint The test 'continuous_marker' has an AUC of 0.955 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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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
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 53.7661964 86.95652 94.54545 97.08738 77.61194 0.8150198 0.9549407 0.8150198
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────────────
#> continuous_marker 0.9549407 0.9258927 0.9839887 < .0000001
#> ────────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
