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Advanced TNM Stage Migration Analysis

Source: R/stagemigration.b.R
stagemigrationClass.Rd

State-of-the-art analysis for validating TNM staging system improvements using comprehensive statistical methods. This analysis provides pathologists with robust tools to evaluate whether a new staging system provides superior prognostic discrimination compared to existing systems.

Value

A comprehensive staging validation analysis with statistical comparisons, clinical interpretation, and advanced visualizations

Details

This comprehensive staging validation analysis includes:

Core Migration Analysis:

  • Migration matrices with detailed statistics

  • Stage distribution comparisons

  • Will Rogers phenomenon detection

  • Upstaging and downstaging quantification

Advanced Discrimination Metrics:

  • Harrell's C-index with confidence intervals

  • Net Reclassification Improvement (NRI)

  • Integrated Discrimination Improvement (IDI)

  • Time-dependent ROC analysis

  • Likelihood ratio tests for nested models

Clinical Utility Assessment:

  • Decision Curve Analysis (DCA)

  • Net benefit calculations

  • Clinical significance thresholds

  • Cancer-type specific interpretations

Validation Framework:

  • Bootstrap validation with optimism correction

  • Cross-validation options

  • Stability assessment

  • Internal validation metrics

Advanced Visualizations:

  • Migration heatmaps with flow statistics

  • Time-dependent ROC curves

  • Calibration plots

  • Decision curves

  • Forest plots with confidence intervals

PHASE 1 ENHANCEMENTS - Evidence-Based Assessment Framework:

  • Will Rogers Evidence Assessment: Multi-criteria evaluation framework

  • Migration Pattern Analysis: Advanced flow statistics and retention rates

  • Survival Pattern Validation: Upstaged patient survival similarity analysis

  • Biological Consistency Checks: Risk factor profile assessments

  • Landmark Analysis Integration: Time-based cutoff discrimination analysis

  • Clinical Decision Support: Evidence-based implementation recommendations

  • Traffic Light Assessment: PASS/BORDERLINE/CONCERN/FAIL evidence grading

  • Enhanced Heatmap Analytics: Major flow identification and net migration analysis

Clinical Applications

  • TNM staging system validation (7th to 8th edition transitions)

  • AJCC staging improvements

  • Institution-specific staging modifications

  • Multi-institutional staging harmonization

  • Biomarker-enhanced staging systems

Statistical Methods

The analysis implements state-of-the-art methods for staging validation:

  • NRI: Quantifies net improvement in risk classification

  • IDI: Measures integrated discrimination improvement

  • C-index: Harrell's concordance with bootstrap confidence intervals

  • DCA: Clinical utility across decision thresholds

  • Bootstrap: Internal validation with bias correction

Clinical Decision Framework

Results include comprehensive guidance for staging system adoption:

  • Statistical significance vs. clinical importance

  • Effect size interpretation (small, medium, large improvements)

  • Sample size adequacy assessment

  • Recommendation confidence levels

  • Implementation considerations

Data Requirements

  • Sample Size: Minimum 30 patients (100+ recommended)

  • Follow-up: Adequate survival time for meaningful analysis

  • Staging: Both old and new staging variables with 2+ levels

  • Events: Binary event indicator (0/1) or factor with specified level

  • Data Quality: Complete case analysis (missing values removed)

Troubleshooting

  • "TRUE/FALSE error": Check for missing values in staging or survival variables

  • "Not atomic error": Disable individual tables to isolate problematic components

  • Model fitting errors: Ensure adequate sample size and event rate (5-95%)

  • Stage level errors: Verify staging variables have multiple distinct levels

See also

concordance for C-index calculations, ggsurvplot for survival visualizations

Super classes

jmvcore::Analysis -> ClinicoPath::stagemigrationBase -> stagemigrationClass

Methods

Public methods

Inherited methods

Method clone()

The objects of this class are cloneable with this method.

Usage

stagemigrationClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Basic staging comparison
stagemigration(
  data = cancer_data,
  oldStage = "old_stage",
  newStage = "new_stage",
  survivalTime = "survival_months",
  event = "outcome",
  eventLevel = "DEAD",
  analysisType = "basic"
)

# Comprehensive analysis with all options
stagemigration(
  data = lung_cancer_cohort,
  oldStage = "tnm7_stage",
  newStage = "tnm8_stage",
  survivalTime = "os_months",
  event = "death",
  eventLevel = "dead",
  analysisType = "comprehensive",
  calculateNRI = TRUE,
  performBootstrap = TRUE,
  bootstrapReps = 1000
)

# PHASE 1 ENHANCED: Evidence-based Will Rogers assessment
stagemigration(
  data = pancreatic_cohort,
  oldStage = "T_AJCC8",
  newStage = "T_modified", 
  survivalTime = "overall_survival_months",
  event = "death_status",
  eventLevel = "Dead",
  analysisType = "publication",
  advancedMigrationAnalysis = TRUE,
  showMigrationHeatmap = TRUE,
  cancerType = "other",
  showExplanations = TRUE
)

# Phase 1 Enhanced with landmark analysis for lung cancer
stagemigration(
  data = lung_staging_data,
  oldStage = "stage_7th_edition",
  newStage = "stage_8th_edition",
  survivalTime = "survival_months",
  event = "vital_status",
  eventLevel = "deceased",
  analysisType = "comprehensive", 
  advancedMigrationAnalysis = TRUE,
  cancerType = "lung",  # Uses lung-specific landmark times: 3,6,12,24 months
  showWillRogersVisualization = TRUE,
  showMigrationSurvivalComparison = TRUE
)
} # }