psychopdaROC Overlapping Distributions Data
Source:R/data_psychopdaROC_docs.R
psychopdaROC_overlap.RdDataset with 190 patients showing moderate overlap between diseased and non-diseased groups, representing realistic clinical scenarios with imperfect discrimination.
Format
A data frame with 190 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT190)
- diagnosis
Factor: "Diseased" or "Non_Diseased" (40%/60% prevalence)
- test_value
Numeric: Test values with moderate overlap (mean: 60 vs 55, SD 18)
Details
Small mean difference (5 units) with substantial overlap between groups. Represents realistic clinical scenarios where diagnostic tests show moderate but imperfect discrimination (AUC ~0.60-0.65).
Examples
data(psychopdaROC_overlap)
psychopdaROC(data = psychopdaROC_overlap, dependentVars = "test_value",
classVar = "diagnosis", positiveClass = "Diseased",
refVar = "test_value")
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> ADVANCED ROC ANALYSIS
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#>
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#> Procedure Notes
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#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
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#>
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#> Measure Variable(s): test_value
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#> Class Variable: diagnosis
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#> Positive Class: Diseased
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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
#>
#> Seed: 123Positive Class: Diseased (Prevalence: 38.9%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> test_value 0.5994874 0.5176626 0.6813122 0.0171704
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> test_value Poor Not recommended as standalone diagnostic marker The test 'test_value' has an AUC of 0.599 indicating poor discriminatory ability. Alternative diagnostic approaches should be considered.
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 55.4732866 64.86486 55.17241 48.00000 71.11111 0.2003728 0.5994874 0.2003728
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> test_value 0.5994874 0.5176626 0.6813122 0.0171704
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
