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")
#>
#> ADVANCED ROC ANALYSIS
#>
#>
#>
#>
#> Procedure Notes
#>
#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
#>
#>
#>
#> Measure Variable(s): test_a, test_b
#>
#> Class Variable: diagnosis
#>
#> Positive Class: Disease
#>
#>
#>
#> Method: maximize_metric
#>
#> All Observed Cutpoints: FALSE
#>
#> Metric: youden
#>
#> Direction (relative to cutpoint): >=
#>
#> Tie Breakers: mean
#>
#> Metric Tolerance: 1e-06
#>
#>
#>
#> <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: 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
#> ──────────────────────────────────────────────────────────────────────
#> Note. Reading of the test values: <b>HIGHER values were taken to
#> indicate Disease</b> (Classification Direction = ">="). If that
#> is the wrong way round for this marker, every sensitivity,
#> specificity, cutpoint and AUC below is reversed — switch
#> Classification Direction to "<=" and the AUC becomes 1 minus the
#> value shown.
#>
#>
#> 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.
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 59.8074173 71.64179 87.30159 85.71429 74.32432 0.5894338 0.8488510 0.5894338
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 61.9134099 61.76471 87.69231 84.00000 68.67470 0.4945701 0.7755656 0.4945701
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ──────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────
#> test_a 0.8488510
#> test_b 0.7755656
#> ──────────────────────────────────────────────────────────────────────
#> Note. Reading of the test values: <b>HIGHER values were taken to
#> indicate Disease</b> (Classification Direction = ">="). If that
#> is the wrong way round for this marker, every sensitivity,
#> specificity, cutpoint and AUC below is reversed — switch
#> Classification Direction to "<=" and the AUC becomes 1 minus the
#> value shown.
#>
