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 10× 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",
method = "oc_cost_ratio")
#> Error in psychopdaROC(data = psychopdaROC_costbenefit, dependentVars = "risk_score", classVar = "outcome", positiveClass = "Event", method = "oc_cost_ratio"): argument "refVar" is missing, with no default