psychopdaROC Time-Dependent Biomarker Data
Source:R/data_psychopdaROC_docs.R
psychopdaROC_timedep.RdDataset with 140 patients featuring baseline and follow-up biomarker measurements for evaluating time-dependent diagnostic performance.
Format
A data frame with 140 rows and 5 variables:
- patient_id
Character: Patient identifier (PT001-PT140)
- outcome
Factor: "Event" or "No_Event" (32%/68% prevalence)
- baseline_marker
Numeric: Baseline biomarker (mean: 70 for event, 50 for no event)
- followup_marker
Numeric: Follow-up biomarker (increases for events, decreases for no events)
- time_to_outcome
Numeric: Time to outcome in months (1-36)
Details
Follow-up marker changes from baseline: increases by ~15 for events, decreases by ~5 for no events. Enables ROC analysis with change scores or follow-up values.
Examples
data(psychopdaROC_timedep)
psychopdaROC(data = psychopdaROC_timedep,
dependentVars = c("baseline_marker", "followup_marker"),
classVar = "outcome", positiveClass = "Event",
refVar = "baseline_marker")
#> 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): baseline_marker, followup_marker
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#> Class Variable: outcome
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#> Positive Class: Event
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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;'>
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#> Analysis Status
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#> Seed: 123Positive Class: Event (Prevalence: 27.9%)Analysis Mode: Basic
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#> ROC Analysis Summary
#> ──────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────────────
#> baseline_marker 0.8565626 0.7834935 0.9296316 < .0000001
#> followup_marker 0.9548109 0.9158621 0.9937596 < .0000001
#> ──────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
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#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> baseline_marker Good Suitable for clinical use with appropriate cutpoint The test 'baseline_marker' has an AUC of 0.857 indicating good discriminatory ability. This test performs well for clinical decision making.
#> followup_marker Excellent Suitable for clinical use with appropriate cutpoint The test 'followup_marker' has an AUC of 0.955 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#> OPTIMAL CUTPOINTS AND PERFORMANCE
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#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 64.6200867 76.92308 82.17822 62.50000 90.21739 0.5910129 0.8565626 0.5910129
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 65.5593033 97.43590 84.15842 70.37037 98.83721 0.8159431 0.9548109 0.8159431
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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#> Area Under the ROC Curve
#> ──────────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────────────
#> baseline_marker 0.8565626 0.7834935 0.9296316 < .0000001
#> followup_marker 0.9548109 0.9158621 0.9937596 < .0000001
#> ──────────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
