Dataset for subgroup ROC analysis with 200 patients stratified by age group and sex, allowing evaluation of test performance across demographic subgroups.
Format
A data frame with 200 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT200)
- disease
Factor: "Disease" or "Healthy" (30%/70% prevalence)
- test_score
Numeric: Diagnostic test score (mean: 70 for disease, 45 for healthy)
- age_group
Factor: "Young", "Middle", or "Elderly"
- sex
Factor: "Male" or "Female"
Details
Enables subgroup-specific ROC analysis to assess whether test performance varies across age groups or between sexes. Useful for evaluating test generalizability across populations.
Examples
data(psychopdaROC_subgroup)
psychopdaROC(data = psychopdaROC_subgroup,
dependentVars = "test_score", classVar = "disease",
positiveClass = "Disease", refVar = "test_score",
subGroup = "age_group")
#> Multiple optimal cutpoints found, applying break_ties.
#> 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): test_score
#>
#> Class Variable: disease
#>
#> Positive Class: Disease
#>
#> Subgroup Variable: age_group
#>
#>
#>
#> Method: maximize_metric
#>
#> All Observed Cutpoints: FALSE
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#> Metric: youden
#>
#> Direction (relative to cutpoint): >=
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#> Tie Breakers: mean
#>
#> 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
#>
#> Seed: 123Positive Class: Disease (Prevalence: 28.5%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────────────
#> test_score ::: Elderly 0.7889610 0.6760838 0.9018383 0.0000005
#> test_score ::: Young 0.9189542 0.8487080 0.9892005 < .0000001
#> test_score ::: Middle 0.8869396 0.8113542 0.9625250 < .0000001
#> ─────────────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
#>
#> Clinical Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────
#> ───────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 51.2738048 85.71429 56.81818 48.64865 89.28571 0.4253247 0.7889610 0.4253247
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 61.0577406 88.23529 82.22222 65.21739 94.87179 0.7045752 0.9189542 0.7045752
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 50.2753644 94.73684 79.62963 62.06897 97.72727 0.7436647 0.8869396 0.7436647
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────────────────
#> test_score ::: Elderly 0.7889610 0.6760838 0.9018383 0.0000005
#> test_score ::: Young 0.9189542 0.8487080 0.9892005 < .0000001
#> test_score ::: Middle 0.8869396 0.8113542 0.9625250 < .0000001
#> ─────────────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
