Dataset with 160 patients including cost information for false positive and false negative errors, enabling cost-benefit optimal cutpoint determination.
Format
A data frame with 160 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT160)
- outcome
Factor: "Event" or "No_Event" (28%/72% prevalence)
- risk_score
Numeric: Risk prediction score (mean: 75 for event, 50 for no event)
- false_positive_cost
Numeric: Cost of false positive ($100)
- false_negative_cost
Numeric: Cost of false negative ($1000)
Details
False negative cost is 10x higher than false positive cost, representing clinical scenarios where missing a case is much more costly than a false alarm. Enables cost-ratio optimal cutpoint method.
Examples
data(psychopdaROC_costbenefit)
psychopdaROC(data = psychopdaROC_costbenefit, dependentVars = "risk_score",
classVar = "outcome", positiveClass = "Event",
refVar = "risk_score",
method = "oc_cost_ratio")
#>
#> ADVANCED ROC ANALYSIS
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#> Procedure Notes
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#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
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#>
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#> Measure Variable(s): risk_score
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#> Class Variable: outcome
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#> Positive Class: Event
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#> Method: oc_cost_ratio
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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;'>
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#> Analysis Status
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#> Seed: 123Positive Class: Event (Prevalence: 26.2%)Analysis Mode: Basic
#>
#> ROC Analysis Summary
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> risk_score 0.7781477 0.6980973 0.8594088 < .0000001
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> risk_score Fair May be useful in combination with other markers The test 'risk_score' has an AUC of 0.778 indicating fair discriminatory ability. Consider combining with other clinical information.
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 76.3000000 50.00000 89.83051 63.63636 83.46457 0.3983051 0.7781477 0.3983051
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ─────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ─────────────────────────────────────────────────────────────────────────
#> risk_score 0.7781477 0.6980973 0.8594088 < .0000001
#> ─────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
