Dataset with 80 patients where the biomarker has no variation (constant value), testing handling of predictors with zero variance.
Format
A data frame with 80 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT080)
- outcome
Factor: "Positive" or "Negative" (50%/50% prevalence)
- constant_marker
Numeric: Constant value of 50 for all patients
Details
All patients have identical biomarker value (50). Tests error handling for zero-variance predictors that cannot produce ROC curves.
Examples
data(psychopdaROC_constant)
psychopdaROC(data = psychopdaROC_constant, dependentVars = "constant_marker",
classVar = "outcome", positiveClass = "Positive",
refVar = "constant_marker")
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> ADVANCED ROC ANALYSIS
#>
#>
#>
#>
#> Procedure Notes
#>
#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
#>
#>
#>
#> Measure Variable(s): constant_marker
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#> Class Variable: outcome
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#> Positive Class: Positive
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#>
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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): >=
#>
#> 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: Positive (Prevalence: 61.3%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────
#> constant_marker 0.5000000 0.5000000 0.5000000
#> ─────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
#>
#> Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> constant_marker Poor Not recommended as standalone diagnostic marker The test 'constant_marker' has an AUC of 0.500 indicating poor discriminatory ability. Alternative diagnostic approaches should be considered.
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Inf 100.00000 0.00000 61.25000 0.00000 0.0000000 0.5000000 0.0000000
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────
#> constant_marker 0.5000000 0.5000000 0.5000000
#> ─────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
