Dataset with 150 patients showing no discrimination between case and control groups (AUC ~0.50), useful for testing handling of ineffective biomarkers.
Format
A data frame with 150 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT150)
- status
Factor: "Case" or "Control" (50%/50% prevalence)
- poor_marker
Numeric: Biomarker with no discriminatory value (mean 50, SD 15)
Details
Both cases and controls have identical distributions (normal, mean=50, SD=15). Tests proper handling and warning messages for biomarkers with no diagnostic value.
Examples
data(psychopdaROC_poor)
psychopdaROC(data = psychopdaROC_poor, dependentVars = "poor_marker",
classVar = "status", positiveClass = "Case",
refVar = "poor_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): poor_marker
#>
#> Class Variable: status
#>
#> Positive Class: Case
#>
#>
#>
#> Method: maximize_metric
#>
#> All Observed Cutpoints: FALSE
#>
#> Metric: youden
#>
#> Direction (relative to cutpoint): >=
#>
#> Tie Breakers: mean
#>
#> Metric Tolerance: 0.05
#>
#>
#>
#> <hr />
#>
#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
#>
#> Seed: 123Positive Class: Case (Prevalence: 43.3%)Analysis Mode: Basic
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> poor_marker 0.4761991 0.3826024 0.5697958 0.6181997
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> poor_marker Poor Not recommended as standalone diagnostic marker The test 'poor_marker' has an AUC of 0.476 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
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 35.5500932 83.07692 16.47059 43.20000 56.00000 -0.0045249 0.4761991 -0.0045249
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> poor_marker 0.4761991 0.3826024 0.5697958 0.6181997
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
