Large dataset with 500 patients and multiple biomarkers for testing computational efficiency and performance with substantial sample sizes.
Format
A data frame with 500 rows and 8 variables:
- patient_id
Character: Patient identifier (PT0001-PT0500)
- disease_status
Factor: "Disease" or "No_Disease" (30%/70% prevalence)
- biomarker1
Numeric: First biomarker (mean: 75 for disease, 50 for no disease)
- biomarker2
Numeric: Second biomarker (mean: 68 for disease, 48 for no disease)
- age
Numeric: Patient age in years (mean 62, SD 13)
- sex
Factor: "Male" or "Female"
- site
Factor: Research site (Site_1 through Site_10)
- risk_category
Factor: "Low", "Intermediate", or "High"
Details
Large sample (n=500) with multiple biomarkers and stratification variables. Tests computational efficiency and stability of estimates with adequate sample sizes. Includes multi-site and risk stratification for subgroup analysis.
Examples
data(psychopdaROC_large)
psychopdaROC(data = psychopdaROC_large,
dependentVars = c("biomarker1", "biomarker2"),
classVar = "disease_status", positiveClass = "Disease",
refVar = "biomarker1")
#> Multiple optimal cutpoints found, applying break_ties.
#> 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): biomarker1, biomarker2
#>
#> Class Variable: disease_status
#>
#> Positive Class: Disease
#>
#>
#>
#> 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: Disease (Prevalence: 32.8%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> biomarker1 0.8080539 0.7676279 0.8484798 < .0000001
#> biomarker2 0.7915941 0.7512573 0.8319309 < .0000001
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> biomarker1 Good Suitable for clinical use with appropriate cutpoint The test 'biomarker1' has an AUC of 0.808 indicating good discriminatory ability. This test performs well for clinical decision making.
#> biomarker2 Fair May be useful in combination with other markers The test 'biomarker2' has an AUC of 0.792 indicating fair discriminatory ability. Consider combining with other clinical information.
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 63.8980591 65.24390 80.65476 62.20930 82.62195 0.4589866 0.8080539 0.4589866
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 55.1077643 69.51220 67.85714 51.35135 82.01439 0.3736934 0.7915941 0.3736934
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> biomarker1 0.8080539 0.7676279 0.8484798 < .0000001
#> biomarker2 0.7915941 0.7512573 0.8319309 < .0000001
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
