Small dataset with only 30 patients for testing performance with limited sample sizes and assessing stability of cutpoint estimates.
Format
A data frame with 30 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT030)
- class
Factor: "Positive" or "Negative" (50%/50% prevalence)
- marker
Numeric: Biomarker value (mean: 65 for positive, 45 for negative)
Details
Limited sample size (n=30) tests stability of ROC analysis and cutpoint determination with small datasets. Wide confidence intervals expected.
Examples
data(psychopdaROC_small)
psychopdaROC(data = psychopdaROC_small, dependentVars = "marker",
classVar = "class", positiveClass = "Positive",
refVar = "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): marker
#>
#> Class Variable: class
#>
#> Positive Class: Positive
#>
#>
#>
#> 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: Positive (Prevalence: 70%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ───────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ───────────────────────────────────────────────────────────────────────
#> marker 0.8835979 0.7541782 1.0000000 < .0000001
#> ───────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> marker Good Suitable for clinical use with appropriate cutpoint The test 'marker' has an AUC of 0.884 indicating good discriminatory ability. This test performs well for clinical decision making.
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 56.2681789 76.19048 77.77778 88.88889 58.33333 0.5396825 0.8835979 0.5396825
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ───────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ───────────────────────────────────────────────────────────────────────
#> marker 0.8835979 0.7541782 1.0000000 < .0000001
#> ───────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
