Comprehensive evaluation of biomarker predictive performance using both time-dependent and general binary ROC analysis. Supports multiple ROC estimation methods, statistical comparisons, and provides clinical interpretation of results.
Details
This analysis provides both time-dependent and binary ROC curve analysis with:
Time-dependent ROC: Multiple estimation methods (incident, cumulative, static)
Binary ROC: General diagnostic performance evaluation
DeLong test for comparing multiple ROC curves (binary mode)
Bootstrap and Venkatraman tests for ROC comparison
Bootstrap confidence intervals for robust inference
Optimal cutoff calculation using Youden index
Comprehensive visualization (ROC curves and AUC over time)
Clinical interpretation and performance assessment
Model comparison capabilities with statistical testing
Super classes
jmvcore::Analysis -> ClinicoPath::timerocBase -> timerocClass
Methods
Inherited methods
jmvcore::Analysis$.createImage()jmvcore::Analysis$.createImages()jmvcore::Analysis$.createPlotObject()jmvcore::Analysis$.load()jmvcore::Analysis$.render()jmvcore::Analysis$.save()jmvcore::Analysis$.savePart()jmvcore::Analysis$.setCheckpoint()jmvcore::Analysis$.setParent()jmvcore::Analysis$.setReadDatasetHeaderSource()jmvcore::Analysis$.setReadDatasetSource()jmvcore::Analysis$.setResourcesPathSource()jmvcore::Analysis$.setStatePathSource()jmvcore::Analysis$addAddon()jmvcore::Analysis$asProtoBuf()jmvcore::Analysis$asSource()jmvcore::Analysis$check()jmvcore::Analysis$init()jmvcore::Analysis$optionsChangedHandler()jmvcore::Analysis$postInit()jmvcore::Analysis$print()jmvcore::Analysis$readDataset()jmvcore::Analysis$run()jmvcore::Analysis$serialize()jmvcore::Analysis$setError()jmvcore::Analysis$setStatus()jmvcore::Analysis$translate()ClinicoPath::timerocBase$initialize()