Clinical ROC Analysis — enhancedROC • meddecide

Clinical ROC analysis toolkit for comprehensive diagnostic performance evaluation. Includes ROC curve analysis, Youden Index optimization, sensitivity/specificity analysis, optimal cutoff determination, and comparative ROC analysis. Essential for biomarker validation, diagnostic test evaluation, and clinical decision support in medical research.

Usage

enhancedROC(
  data,
  outcome,
  positiveClass,
  predictors,
  analysisType = "single",
  direction = "auto",
  youdenOptimization = TRUE,
  customCutoffs,
  sensitivityThreshold = 0.8,
  specificityThreshold = 0.8,
  confidenceLevel = 95,
  bootstrapSamples = 1000,
  useBootstrap = TRUE,
  pairwiseComparisons = TRUE,
  comparisonMethod = "delong",
  rocCurve = TRUE,
  aucTable = TRUE,
  cutoffTable = TRUE,
  optimalCutoffs = TRUE,
  diagnosticMetrics = TRUE,
  clinicalMetrics = TRUE,
  smoothMethod = "none",
  partialAuc = FALSE,
  partialRange = "0.8,1.0",
  prevalence = 0.1,
  clinicalContext = "general",
  clinicalPresets = "custom",
  comprehensive_output = FALSE,
  clinical_interpretation = TRUE,
  plotTheme = "clinical",
  plotWidth = 600,
  plotHeight = 600,
  showCutoffPoints = TRUE,
  showConfidenceBands = FALSE,
  showMetricsDiff = TRUE,
  statisticalComparison = TRUE
)

Arguments

data: The data as a data frame.
outcome: Binary outcome variable (disease status)
positiveClass: Select which level represents the positive class (disease/condition present)
predictors: Numeric predictor variables for ROC analysis
analysisType: Type of ROC analysis to perform
direction: Direction of the predictor-outcome relationship
youdenOptimization: Find optimal cutoff using Youden Index (Sensitivity + Specificity - 1)
customCutoffs: Comma-separated list of custom cutoffs to evaluate (e.g., 0.1, 0.5, 0.9)
sensitivityThreshold: Minimum required sensitivity for screening applications
specificityThreshold: Minimum required specificity for confirmatory testing
confidenceLevel: Confidence level for AUC confidence intervals
bootstrapSamples: Number of bootstrap samples for confidence intervals
useBootstrap: Use bootstrap methods for confidence intervals
pairwiseComparisons: Perform pairwise comparisons between ROC curves
comparisonMethod: Method for comparing ROC curves
rocCurve: Display ROC curve plot
aucTable: Display AUC summary table
cutoffTable: Display detailed cutoff analysis
optimalCutoffs: Display optimal cutoff summary
diagnosticMetrics: Display comprehensive diagnostic metrics
clinicalMetrics: Display clinical application metrics (PPV, NPV, LR+, LR-)
smoothMethod: Method for smoothing ROC curves
partialAuc: Calculate partial AUC for specific sensitivity/specificity ranges
partialRange: Range for partial AUC (specificity_min,specificity_max)
prevalence: Disease prevalence for calculating predictive values
clinicalContext: Clinical application context for interpretation
clinicalPresets: Pre-configured settings for common clinical scenarios
comprehensive_output: Include comprehensive statistical details
clinical_interpretation: Provide clinical context for ROC analysis results
plotTheme: Visual theme for ROC plots
plotWidth: Width of ROC plots
plotHeight: Height of ROC plots
showCutoffPoints: Highlight optimal cutoff points on ROC curve
showConfidenceBands: Display confidence bands around ROC curve
showMetricsDiff: Display detailed differences between model metrics
statisticalComparison: Perform comprehensive statistical comparison between models

Value

A results object containing:

`results$results$instructions`					a html
`results$results$analysisSummary`					Plain language summary of key findings
`results$results$clinicalReport`					Copy-ready clinical report sentences for publications and reports
`results$results$aucSummary`					AUC values with confidence intervals for each predictor
`results$results$rocComparisons`					Pairwise comparisons between ROC curves
`results$results$detailedComparison`					Comprehensive comparison of diagnostic metrics between models
`results$results$statisticalSummary`					Summary of statistical tests for model comparison
`results$results$optimalCutoffSummary`					Youden Index optimization results for each predictor
`results$results$cutoffAnalysis`					Comprehensive analysis across multiple cutoff values
`results$results$diagnosticPerformance`					Comprehensive diagnostic performance measures at optimal cutoff
`results$results$clinicalApplicationMetrics`					Clinical metrics including predictive values and likelihood ratios
`results$results$partialAucAnalysis`					Partial AUC analysis for specific sensitivity/specificity ranges
`results$results$comprehensiveAnalysisSummary`					Enhanced statistical summary for comprehensive output
`results$results$clinicalInterpretationGuide`					a html
`results$results$methodsExplanation`					a html
`results$results$rocCurvePlot`					ROC curves with optimal cutoff points
`results$results$comparativeROCPlot`					Multiple ROC curves for comparison
`results$results$cutoffAnalysisPlot`					Sensitivity and specificity across cutoff values
`results$results$youdenIndexPlot`					Youden Index values across cutoff range
`results$results$clinicalDecisionPlot`					Clinical decision curves and threshold analysis