Multiple biomarker comparison dataset with 220 patients featuring three individual markers and a combined score for ROC analysis and marker comparison.
Format
A data frame with 220 rows and 6 variables:
- patient_id
Character: Patient identifier (PT001-PT220)
- diagnosis
Factor: "Positive" or "Negative" (35%/65% prevalence)
- marker1
Numeric: First biomarker (mean: 100 for positive, 70 for negative)
- marker2
Numeric: Second biomarker (mean: 85 for positive, 55 for negative)
- marker3
Numeric: Third biomarker (mean: 90 for positive, 65 for negative)
- combined_score
Numeric: Average of three markers
Details
Designed for comparing individual biomarker performance and evaluating combined marker strategies. The combined score typically shows improved discrimination compared to individual markers.
Examples
data(psychopdaROC_multibiomarker)
psychopdaROC(data = psychopdaROC_multibiomarker,
dependentVars = c("marker1", "marker2", "marker3", "combined_score"),
classVar = "diagnosis", positiveClass = "Positive",
refVar = "marker1",
clinicalMode = "comprehensive")
#> Multiple optimal cutpoints found, applying break_ties.
#> Multiple optimal cutpoints found, applying break_ties.
#> Multiple optimal cutpoints found, applying break_ties.
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> ADVANCED ROC ANALYSIS
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#> Procedure Notes
#>
#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
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#>
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#> Measure Variable(s): marker1, marker2, marker3, combined_score
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#> Class Variable: diagnosis
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#> Positive Class: Positive
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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 />
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#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
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#> Analysis Status
#>
#> Seed: 123Positive Class: Positive (Prevalence: 33.2%)Analysis Mode:
#> Comprehensive
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────
#> marker1 0.8214519 0.7624193 0.8804845 < .0000001
#> marker2 0.8895723 0.8429232 0.9362213 < .0000001
#> marker3 0.8276023 0.7722325 0.8829720 < .0000001
#> combined_score 0.9729755 0.9516340 0.9943170 < .0000001
#> ─────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
#>
#> Clinical Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> marker1 Good Suitable for clinical use with appropriate cutpoint The test 'marker1' has an AUC of 0.821 indicating good discriminatory ability. This test performs well for clinical decision making.
#> marker2 Good Suitable for clinical use with appropriate cutpoint The test 'marker2' has an AUC of 0.890 indicating good discriminatory ability. This test performs well for clinical decision making.
#> marker3 Good Suitable for clinical use with appropriate cutpoint The test 'marker3' has an AUC of 0.828 indicating good discriminatory ability. This test performs well for clinical decision making.
#> combined_score Excellent Suitable for clinical use with appropriate cutpoint The test 'combined_score' has an AUC of 0.973 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
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#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 87.2496589 78.08219 77.55102 63.33333 87.69231 0.5563321 0.8214519 0.5563321
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 72.2080895 79.45205 82.31293 69.04762 88.97059 0.6176498 0.8895723 0.6176498
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 75.9021153 82.19178 75.51020 62.50000 89.51613 0.5770198 0.8276023 0.5770198
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 78.7908214 87.67123 93.19728 86.48649 93.83562 0.8086851 0.9729755 0.8086851
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────
#> marker1 0.8214519 0.7624193 0.8804845 < .0000001
#> marker2 0.8895723 0.8429232 0.9362213 < .0000001
#> marker3 0.8276023 0.7722325 0.8829720 < .0000001
#> combined_score 0.9729755 0.9516340 0.9943170 < .0000001
#> ─────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
