psychopdaROC Screening Data - Cancer Detection
Source:R/data_psychopdaROC_docs.R
psychopdaROC_screening.RdCancer screening dataset with 250 patients featuring multiple biomarkers (PSA and CA125) for evaluating screening test performance with low disease prevalence (15%).
Format
A data frame with 250 rows and 6 variables:
- patient_id
Character: Patient identifier (PT001-PT250)
- cancer
Factor: "Cancer" or "No_Cancer" (15%/85% prevalence)
- psa_level
Numeric: PSA level (ng/mL), log-normal distribution
- ca125
Numeric: CA125 level (U/mL), higher in cancer cases
- age
Numeric: Patient age in years (mean 65, SD 10)
- risk_factors
Factor: "None", "Family_History", or "Multiple"
Details
Designed for evaluating screening test characteristics where high sensitivity is prioritized. PSA levels are log-normally distributed (median: 12 for cancer, 4 for no cancer). CA125 shows normal distribution with higher values in cancer cases (mean: 65 vs 25).
Examples
data(psychopdaROC_screening)
psychopdaROC(data = psychopdaROC_screening,
dependentVars = c("psa_level", "ca125"),
classVar = "cancer", positiveClass = "Cancer",
refVar = "psa_level",
clinicalPreset = "screening")
#> Multiple optimal cutpoints found, applying break_ties.
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> 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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#> Measure Variable(s): psa_level, ca125
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#> Class Variable: cancer
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#> Positive Class: Cancer
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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 />
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#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
#>
#> Seed: 123Positive Class: Cancer (Prevalence: 13.2%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> psa_level 0.8762044 0.8174093 0.9349996 < .0000001
#> ca125 0.8883536 0.8199794 0.9567277 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> psa_level Good Suitable for clinical use with appropriate cutpoint The test 'psa_level' has an AUC of 0.876 indicating good discriminatory ability. This test performs well for clinical decision making.
#> ca125 Good Suitable for clinical use with appropriate cutpoint The test 'ca125' has an AUC of 0.888 indicating good discriminatory ability. This test performs well for clinical decision making.
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 8.2015789 78.78788 80.64516 38.23529 96.15385 0.5943304 0.8762044 0.5943304
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 43.0666667 78.78788 85.71429 45.61404 96.37306 0.6450216 0.8883536 0.6450216
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> psa_level 0.8762044 0.8174093 0.9349996 < .0000001
#> ca125 0.8883536 0.8199794 0.9567277 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
