Dataset with 150 patients including missing values in predictors and class variable for testing handling of incomplete data.
Format
A data frame with 150 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT150)
- diagnosis
Factor: "Disease" or "Healthy" with ~5% missing
- test_a
Numeric: First test with ~8% missing
- test_b
Numeric: Second test with ~7% missing
- covariate
Factor: "A", "B", or "C"
Details
Missing data introduced randomly: diagnosis (8 missing), test_a (12 missing), test_b (10 missing). Tests proper handling of missing values in ROC analysis with appropriate warnings or exclusions.
Examples
data(psychopdaROC_missing)
psychopdaROC(data = psychopdaROC_missing,
dependentVars = c("test_a", "test_b"),
classVar = "diagnosis", positiveClass = "Disease",
refVar = "test_a")
#> 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:
#>
#>
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#> Measure Variable(s): test_a, test_b
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#> Class Variable: diagnosis
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#> Positive Class: Disease
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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 />
#>
#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
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#> Seed: 123Positive Class: Disease (Prevalence: 50.7%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ──────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────
#> test_a 0.8488510
#> test_b 0.7755656
#> ──────────────────────────────────────────────────────────────────────
#>
#>
#> Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> test_a Good Suitable for clinical use with appropriate cutpoint The test 'test_a' has an AUC of 0.849 indicating good discriminatory ability. This test performs well for clinical decision making.
#> test_b Fair May be useful in combination with other markers The test 'test_b' has an AUC of 0.776 indicating fair discriminatory ability. Consider combining with other clinical information.
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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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
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 59.2521263 71.64179 85.71429 84.21053 73.97260 0.5735608 0.8488510 0.5735608
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 60.8374178 61.76471 86.15385 82.35294 68.29268 0.4791855 0.7755656 0.4791855
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ──────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────
#> test_a 0.8488510
#> test_b 0.7755656
#> ──────────────────────────────────────────────────────────────────────
#>
