Advanced TNM Stage Migration Analysis - Comprehensive Guide
Source:vignettes/oncopath-stagemigration-comprehensive.Rmd
oncopath-stagemigration-comprehensive.RmdNot yet released. The
stagemigrationanalysis is on a development menu route, so it does not appear in the jamovi menus of ClinicoPath or of any of its submodules. It is documented here ahead of a future release, and its options, defaults and output may still change. The R function is exported, so the examples below run from an R console; what is not yet available is the jamovi analysis itself.
Note: The
stagemigration()function is designed for use within jamovi’s GUI. The code examples below show the R syntax for reference. To run interactively, usedevtools::load_all()and call the R6 class directly:stagemigrationClass$new(options = stagemigrationOptions$new(...), data = mydata).
Advanced TNM Stage Migration Analysis
Overview
When a new edition of the AJCC/TNM staging manual is released, pathologists and oncologists face a critical question: Does the revised staging system actually provide better prognostic discrimination than the one it replaces? This is not a trivial question. Patients who were Stage II under the old criteria may become Stage III under the new criteria - or vice versa. If the reclassification genuinely separates patients with different prognoses, the new system is an improvement. If it simply reshuffles patients without prognostic benefit, or worse, introduces the Will Rogers phenomenon (where survival appears to improve in every stage simply because of patient reclassification), then the new system may be misleading.
The stagemigration module provides a state-of-the-art
statistical toolkit for answering this question. It goes far beyond a
simple cross-tabulation of old vs. new stages. The analysis includes
formal discrimination metrics (Harrell’s C-index), reclassification
indices (NRI, IDI), decision curve analysis for clinical utility,
bootstrap validation for internal validity, and multiple visualization
tools for presentations and publications. Cancer-type-specific
thresholds and interpretation guidelines are built in for lung, breast,
colorectal, prostate, head and neck, and melanoma.
Whether you are preparing a manuscript evaluating AJCC 8th vs. 7th edition staging, validating an institutional modification to the standard staging system, or assessing a biomarker-enhanced staging proposal, this module provides the full analytical framework recommended by current staging validation literature.
Datasets
The examples in this vignette use synthetic datasets that mimic
realistic TNM staging migration patterns. Because the bundled
.rda files may require regeneration, we create the data
inline to guarantee reproducibility.
set.seed(12345)
# --- Helper functions ---
generate_survival_times <- function(stage, hazard_base = 0.02,
stage_multipliers = c(1, 1.5, 2.5, 4)) {
stage_numeric <- as.numeric(stage)
hazard <- hazard_base * stage_multipliers[stage_numeric]
times <- rexp(length(stage), rate = hazard) * 12 + rnorm(length(stage), 0, 2)
pmax(times, 0.1)
}
generate_censoring <- function(survival_times, censoring_rate = 0.3) {
prob <- pmin(censoring_rate + (survival_times - median(survival_times)) / 100, 0.8)
prob <- pmax(prob, 0.1)
rbinom(length(survival_times), 1, 1 - prob)
}
create_stage_migration <- function(old_stage, migration_prob = 0.25) {
new_stage <- as.numeric(old_stage)
n <- length(old_stage)
migrate <- sample(seq_len(n), size = round(n * migration_prob))
for (i in migrate) {
cs <- new_stage[i]
if (cs == 1) new_stage[i] <- sample(c(1, 2, 3), 1, prob = c(0.4, 0.4, 0.2))
else if (cs == 2) new_stage[i] <- sample(1:4, 1, prob = c(0.2, 0.3, 0.3, 0.2))
else if (cs == 3) new_stage[i] <- sample(2:4, 1, prob = c(0.2, 0.4, 0.4))
else new_stage[i] <- sample(3:4, 1, prob = c(0.1, 0.9))
}
factor(new_stage, levels = 1:4,
labels = c("Stage I", "Stage II", "Stage III", "Stage IV"))
}
# --- Combined dataset (breast + lung + colorectal, N = 2100) ---
make_cohort <- function(n, cancer, hazard_base, mig_prob) {
age <- pmin(pmax(round(rnorm(n, 64, 12)), 30), 90)
sex <- factor(sample(c("Male", "Female"), n, replace = TRUE))
old_num <- sample(1:4, n, replace = TRUE, prob = c(0.28, 0.30, 0.24, 0.18))
old_stage <- factor(old_num, 1:4,
labels = c("Stage I", "Stage II", "Stage III", "Stage IV"))
new_stage <- create_stage_migration(old_stage, migration_prob = mig_prob)
st <- generate_survival_times(new_stage, hazard_base = hazard_base,
stage_multipliers = c(0.7, 1.2, 2.0, 3.5))
ev <- generate_censoring(st, censoring_rate = 0.35)
data.frame(age = age, sex = sex, old_stage = old_stage, new_stage = new_stage,
survival_time = round(st, 1), event = ev, cancer_type = cancer,
stringsAsFactors = FALSE)
}
lung_df <- make_cohort(700, "Lung", 0.015, 0.30)
breast_df <- make_cohort(700, "Breast", 0.008, 0.25)
crc_df <- make_cohort(700, "Colorectal", 0.012, 0.28)
combined_data <- rbind(lung_df, breast_df, crc_df)
combined_data$patient_id <- seq_len(nrow(combined_data))
# --- Small sample dataset (N = 50, edge-case testing) ---
small_data <- make_cohort(50, "Mixed", 0.015, 0.20)
cat("Combined data:", nrow(combined_data), "patients,",
sum(combined_data$event), "events\n")
#> Combined data: 2100 patients, 1155 events
cat("Small data:", nrow(small_data), "patients,",
sum(small_data$event), "events\n")
#> Small data: 50 patients, 26 events| Dataset | N | Key Features |
|---|---|---|
combined_data |
2100 | Combined breast/lung/colorectal, 4-stage system |
lung_df |
700 | Lung cancer specific, higher hazard |
breast_df |
700 | Breast cancer specific, lower hazard |
small_data |
50 | Small sample for edge-case testing |
1. Basic Migration Analysis
The simplest use case: compare two staging systems and display the migration matrix plus an overview table. This is the starting point for any staging validation.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "basic",
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────The migration overview tells you how many patients changed stage (and in which direction), while the migration matrix shows the exact cross-tabulation. Diagonal cells are patients who stayed in the same stage; off-diagonal cells are patients who migrated. Above the diagonal = upstaged, below = downstaged.
2. Analysis Types
The analysisType option controls the scope of the
analysis. There are four levels:
- basic - Migration matrices and distribution tables only
- standard - Adds C-index comparison and NRI
- comprehensive - All statistical methods (default)
- publication - Optimized output formatting for manuscripts
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "standard",
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE,
showStageDistribution = TRUE,
showStatisticalComparison = TRUE,
showMigrationSummary = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Stage Distribution Comparison
#> ─────────────────────────────────────────────────────────────────────────────
#> Stage Original Count Original % New Count New % Change
#> ─────────────────────────────────────────────────────────────────────────────
#> Stage I 591 28.1% 526 25.0% -3.1%
#> Stage II 629 30.0% 609 29.0% -1.0%
#> Stage III 494 23.5% 488 23.2% -0.3%
#> Stage IV 386 18.4% 477 22.7% +4.3%
#> ─────────────────────────────────────────────────────────────────────────────
#>
#>
#> Migration Summary
#> ──────────────────────────────────────────────────────────────
#> Statistic Value
#> ──────────────────────────────────────────────────────────────
#> Overall Migration Rate 14.9% (313/2100)
#> Upstaging Rate 11.7% (245/2100)
#> Downstaging Rate 3.2% (68/2100)
#> Net Migration Effect +177 patients (upward)
#> Chi-square Test χ² = 4138.05, df = 9
#> Chi-square p-value < 2.22e-16
#> Fisher's Exact Test Not calculated
#> Fisher's Exact p-value NA
#> Statistical Significance Highly significant (p < 0.001)
#> ──────────────────────────────────────────────────────────────
#>
#>
#> Statistical Comparison
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value 95% CI Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging C-index 0.6258 [0.6096, 0.6420] Fair discrimination
#> New Staging C-index 0.6452 [0.6295, 0.6610] Fair discrimination
#> C-index Improvement +0.0194 [-0.0032, +0.0420] Small improvement
#> Relative Improvement +3.1% N/A Moderate
#> AIC Difference (Δ) 82.75 N/A Strong evidence for new model
#> BIC Difference (Δ) 82.75 N/A Very strong evidence
#> Clinical Significance No Threshold: 0.020 Below clinical threshold
#> Overall Recommendation 3/4 criteria met N/A Recommended for adoption
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> LR Chi-Square Comparison (Key Staging Validation Metric)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System LR Chi-Square df p-value Goodness of Fit Model Quality
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. LR Chi-Square measures model goodness-of-fit vs null model. Higher values indicate better prognostic
#> discrimination. This is a key metric for staging validation.The stage distribution comparison shows how patient counts shift between systems. The statistical comparison provides C-index values for each staging system so you can see which one discriminates better.
3. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI)
NRI quantifies whether the new staging system correctly reclassifies patients – moving event patients to higher-risk stages and non-event patients to lower-risk stages. IDI measures the integrated improvement in predicted probabilities.
These are the gold-standard metrics for staging validation, recommended by Pencina et al. (2008) and widely used in AJCC staging literature.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
calculateNRI = TRUE,
nriTimePoints = "12, 24, 60",
calculateIDI = TRUE,
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Net Reclassification Improvement (NRI)
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Time Point (months) NRI 95% CI Lower 95% CI Upper NRI+ (Events) NRI- (Non-events) p-value
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 12.00000 -0.021 -0.112 0.070 0.047 -0.067 0.656
#> 24.00000 0.002 -0.069 0.073 0.068 -0.066 0.954
#> 60.00000 -0.001 -0.050 0.047 0.065 -0.066 0.967
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Integrated Discrimination Improvement (IDI)
#> ──────────────────────────────────────────────────────────────────────────────────────────────────
#> IDI 95% CI Lower 95% CI Upper p-value Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────
#> 0.0055614 Modest improvement in discrimination
#> ──────────────────────────────────────────────────────────────────────────────────────────────────Interpreting NRI:
- NRI > 0: Net improvement in classification with the new system
- NRI > 0.20 (the default
nriClinicalThreshold): Clinically meaningful improvement - The event NRI and non-event NRI components tell you whether the improvement comes from better classification of patients who had events, those who did not, or both
Interpreting IDI:
- IDI > 0: Improved discrimination
- IDI represents the increase in the difference between mean predicted probabilities for events and non-events
4. C-index Comparison
Harrell’s concordance index (C-index) measures the staging system’s
ability to rank patients by their survival prognosis. A C-index of 0.5
means random discrimination; 1.0 means perfect discrimination. In
staging validation, a clinically meaningful improvement is typically
0.02 or greater (configurable via
clinicalSignificanceThreshold).
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "standard",
showConcordanceComparison = TRUE,
showStatisticalComparison = TRUE,
includeEffectSizes = TRUE,
clinicalSignificanceThreshold = 0.02
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Statistical Comparison
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value 95% CI Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging C-index 0.6258 [0.6096, 0.6420] Fair discrimination
#> New Staging C-index 0.6452 [0.6295, 0.6610] Fair discrimination
#> C-index Improvement +0.0194 [-0.0032, +0.0420] Small improvement
#> Relative Improvement +3.1% N/A Moderate
#> AIC Difference (Δ) 82.75 N/A Strong evidence for new model
#> BIC Difference (Δ) 82.75 N/A Very strong evidence
#> Clinical Significance No Threshold: 0.020 Below clinical threshold
#> Overall Recommendation 3/4 criteria met N/A Recommended for adoption
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Discrimination Comparison (C-Index)
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model C-Index SE 95% CI Lower 95% CI Upper Difference p-value
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging 0.6257989 0.0082663 0.6095969 0.6420008 . .
#> New Staging 0.6452173 0.0080314 0.6294758 0.6609589 0.019 <0.001
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. P-values for C-index difference are corrected for model correlation using Spearman
#> correlation of risk scores (heuristic approximation). Enable Bootstrap for exact testing.
#>
#>
#> Effect Sizes
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Measure Effect Size Magnitude Interpretation Practical Significance
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cohen's d (C-index difference) 0.9473684 Small Standardized C-index difference: 0.947 Limited practical impact
#> R² equivalent (Original System) 0.0109520 Small Variance explained: 1.1% (C-index: 0.574) Moderate discriminative ability
#> R² equivalent (New System) 0.0169280 Small Variance explained: 1.7% (C-index: 0.592) Moderate discriminative ability
#> Improvement in Discrimination 0.0059760 Negligible 0.6% improvement in variance explained Limited clinical improvement
#> C-index Difference 0.0180000 Small Raw C-index improvement: 0.018 Minimal improvement
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> LR Chi-Square Comparison (Key Staging Validation Metric)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System LR Chi-Square df p-value Goodness of Fit Model Quality
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. LR Chi-Square measures model goodness-of-fit vs null model. Higher values indicate better prognostic
#> discrimination. This is a key metric for staging validation.The concordance comparison table shows:
- C-index for the old staging system
- C-index for the new staging system
- The difference (Delta C) and its confidence interval
- Whether the improvement crosses the clinical significance threshold
5. Will Rogers Effect Detection
The Will Rogers phenomenon occurs when patient reclassification creates the illusion of improvement. Named after Will Rogers’ quip about Oklahomans moving to California and raising the average intelligence of both states, it can make a new staging system appear superior when it is not.
The module detects this by comparing survival within each stage between patients who migrated and those who did not. If migrated patients have systematically different survival than the non-migrated patients in their new stage, the Will Rogers effect is operating.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
showWillRogersAnalysis = TRUE,
showWillRogersVisualization = TRUE,
advancedMigrationAnalysis = TRUE,
showMigrationHeatmap = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Integrated AUC Analysis
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Original System New System Difference 95% CI Lower 95% CI Upper p-value Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Integrated AUC (Trapezoidal) 0.6499000 0.6501000 0.0002000 -0.0251000 0.0276000 0.2500000 Minimal improvement in Integrated AUC (clinically minimal)
#> Mean Time-dependent AUC 0.6479000 0.6446000 -0.0034000 -0.0908000 0.0841000 0.2500000 Minimal deterioration in Mean AUC (clinically minimal)
#> AUC Comparison Test (12m) 0.6411000 0.6274000 -0.0136000 0.4994000 Not significant decline in discrimination
#> AUC Temporal Trend (slope) 0.0002130 0.0003670 0.0001550 0.8043000 No significant temporal trend differences
#> Brier Score (60m) 0.0783000 0.0784000 -0.0001000 Minimal change in combined discrimination/calibration
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #fdf2e9; border-left: 4px solid #f39c12;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Will Rogers
#> Phenomenon Analysis
#>
#> <p style="margin-bottom: 10px;">The Will Rogers phenomenon occurs when
#> patients migrate between stages, potentially creating artificial
#> improvements:
#>
#> <ul style="margin-left: 20px;">
#> Stage: Original staging category being analyzed
#> Unchanged N: Number of patients who remained in the same stage
#> Unchanged Median: Median survival for patients who did not migrate
#> Migrated N: Number of patients who moved to different stages
#> Migrated Median: Median survival for patients who migrated
#> p-value: Statistical significance of survival difference
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> p <0.05 = significant Will Rogers phenomenon detected
#> Migrated patients often have different prognosis than unchanged
#> This can create artificial improvements in apparent survival
#> Must be considered when evaluating new staging systems
#>
#>
#>
#>
#> Will Rogers Phenomenon Analysis
#> ───────────────────────────────────────────────────────────────────────────────────────────────
#> Stage Unchanged N Unchanged Median Migrated N Migrated Median p-value
#> ───────────────────────────────────────────────────────────────────────────────────────────────
#> Stage I 496 3736.5000000 95 577.9000000 < .0000001
#> Stage II 512 1319.8000000 117 378.0000000 0.0027539
#> Stage III 400 487.8000000 94 324.1000000 0.1732342
#> Stage IV 379 248.9000000 7 460.6000000 0.2633548
#> ───────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #fff8e1; border-left: 4px solid #ffc107;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Interpreting the Migration
#> Heatmap
#>
#> <p style="margin-bottom: 10px;">This heatmap visualizes patient
#> movement between staging systems:
#>
#> <ul style="margin-left: 20px;">
#> Y-axis (rows): Original staging system categories
#> X-axis (columns): New staging system categories
#> Color intensity: Darker blue = more patients
#> Numbers: Actual patient counts in each cell
#> Diagonal: Patients who remained in the same stage (no migration)
#>
#> <p style="margin-bottom: 5px;">Reading the heatmap:
#>
#> <ul style="margin-left: 20px;">
#> Cells above the diagonal = downstaging (patients moved to lower
#> stages)
#> Cells below the diagonal = upstaging (patients moved to higher stages)
#> Perfect agreement would show all patients on the diagonal
#> The pattern reveals systematic differences between staging systems
#>
#>
#>
#>
#> Calibration Analysis
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model H-L Chi² H-L df H-L p-value Calibration Slope Calibration Intercept Slope 95% CI Lower Slope 95% CI Upper Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging 11.2454620 2 0.0036148 2.8553312 -1.4654809 2.3030060 3.4143152 H-L test: poor fit; Under-prediction (slope > 1.2); Good overall calibration
#> New Staging 22.0461578 2 0.0000163 2.8840685 -1.5722649 2.4042375 3.3701696 H-L test: poor fit; Under-prediction (slope > 1.2); Systematic over-prediction
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Monotonicity Assessment
#> ──────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System Monotonic Violations Details Monotonicity Score
#> ──────────────────────────────────────────────────────────────────────────────────────────────────
#> Original System Yes 0 Perfect monotonic ordering 1.0000000
#> New System Yes 0 Perfect monotonic ordering 1.0000000
#> ──────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Will Rogers Phenomenon Analysis
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Migration Pattern Count Old Stage Δ Survival New Stage Δ Survival Will Rogers Evidence Clinical Impact
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage I → Stage II 66 3414.1000000 1295.8000000 Possible - Partial pattern Limited bias potential
#> Stage I → Stage III 29 3414.1000000 448.4000000 Possible - Partial pattern Limited bias potential
#> Stage II → Stage I 30 1107.4000000 3758.0000000 Possible - Partial pattern Limited bias potential
#> Stage II → Stage III 52 1107.4000000 448.4000000 Possible - Partial pattern Limited bias potential
#> Stage II → Stage IV 35 1107.4000000 254.8000000 Possible - Partial pattern Limited bias potential
#> Stage III → Stage II 31 448.4000000 1295.8000000 None No significant bias detected
#> Stage III → Stage IV 63 448.4000000 254.8000000 Strong - Classic Will Rogers pattern May artificially improve both stage survivals
#> Stage IV → Stage III 7 253.1000000 448.4000000 Possible - Partial pattern Limited bias potential
#> Overall Assessment 313 0.1490476 Moderate migration - some bias possible Generally acceptable with caveats
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Enhanced Will Rogers Statistical Analysis
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage Period N Median Survival 95% CI Lower 95% CI Upper Δ Survival P-value Test
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage II (original) With vs without 30 migrated patients 629 vs 512 1107.4 vs 1319.8 686.7 vs 932.3 1684.6 vs 2150.4 212.4000000 0.3726057 Log-rank test (Stage II→Stage I migration)
#> Stage I (new) With vs without 30 migrated patients 526 vs 496 3758 vs 3736.5 3356.9 vs 3356.9 6167.9 vs NA -21.5000000 0.8878140 Log-rank test (Stage II→Stage I migration)
#> Stage I (original) With vs without 66 migrated patients 591 vs 496 3414.1 vs 3736.5 2675.3 vs 3356.9 4367.4 vs NA 322.4000000 0.1610650 Log-rank test (Stage I→Stage II migration)
#> Stage II (new) With vs without 66 migrated patients 609 vs 512 1295.8 vs 1319.8 909.4 vs 932.3 2062.5 vs 2150.4 24.0000000 0.8569978 Log-rank test (Stage I→Stage II migration)
#> Stage III (original) With vs without 31 migrated patients 494 vs 400 448.4 vs 487.8 367.7 vs 390.4 528.4 vs 606.1 39.4000000 0.6674910 Log-rank test (Stage III→Stage II migration)
#> Stage II (new) With vs without 31 migrated patients 609 vs 512 1295.8 vs 1319.8 909.4 vs 932.3 2062.5 vs 2150.4 24.0000000 0.8569978 Log-rank test (Stage III→Stage II migration)
#> Stage I (original) With vs without 29 migrated patients 591 vs 496 3414.1 vs 3736.5 2675.3 vs 3356.9 4367.4 vs NA 322.4000000 0.1610650 Log-rank test (Stage I→Stage III migration)
#> Stage III (new) With vs without 29 migrated patients 488 vs 400 448.4 vs 487.8 379.1 vs 390.4 528.4 vs 606.1 39.4000000 0.7403011 Log-rank test (Stage I→Stage III migration)
#> Stage II (original) With vs without 52 migrated patients 629 vs 512 1107.4 vs 1319.8 686.7 vs 932.3 1684.6 vs 2150.4 212.4000000 0.3726057 Log-rank test (Stage II→Stage III migration)
#> Stage III (new) With vs without 52 migrated patients 488 vs 400 448.4 vs 487.8 379.1 vs 390.4 528.4 vs 606.1 39.4000000 0.7403011 Log-rank test (Stage II→Stage III migration)
#> Stage IV (original) With vs without 7 migrated patients 386 vs 379 253.1 vs 248.9 215.4 vs 213 290.5 vs 280.9 -4.2000000 0.8939330 Log-rank test (Stage IV→Stage III migration)
#> Stage III (new) With vs without 7 migrated patients 488 vs 400 448.4 vs 487.8 379.1 vs 390.4 528.4 vs 606.1 39.4000000 0.7403011 Log-rank test (Stage IV→Stage III migration)
#> Stage II (original) With vs without 35 migrated patients 629 vs 512 1107.4 vs 1319.8 686.7 vs 932.3 1684.6 vs 2150.4 212.4000000 0.3726057 Log-rank test (Stage II→Stage IV migration)
#> Stage IV (new) With vs without 35 migrated patients 477 vs 379 254.8 vs 248.9 221.8 vs 213 283.8 vs 280.9 -5.9000000 0.7864914 Log-rank test (Stage II→Stage IV migration)
#> Stage III (original) With vs without 63 migrated patients 494 vs 400 448.4 vs 487.8 367.7 vs 390.4 528.4 vs 606.1 39.4000000 0.6674910 Log-rank test (Stage III→Stage IV migration)
#> Stage IV (new) With vs without 63 migrated patients 477 vs 379 254.8 vs 248.9 221.8 vs 213 283.8 vs 280.9 -5.9000000 0.7864914 Log-rank test (Stage III→Stage IV migration)
#> Overall Assessment 8 migration pattern(s), 14.9% migrated 2100 0.1490476 Moderate migration pattern - check individual tests for Will Rogers evidence
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Detailed Stage-Specific Will Rogers Breakdown
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage Migration Type N Migrated % Migrated Original Median Survival New Median Survival Absolute Improvement Relative Improvement % Improvement Type Clinical Impact
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage I Net Loss 65 10.99831 3414.1000000 3758.0000000 343.9000000 10.07293 Beneficial Will Rogers Effect: Survival improved by losing worst patients
#> Stage II Net Loss 20 3.17965 1107.4000000 1295.8000000 188.4000000 17.01282 Beneficial Will Rogers Effect: Survival improved by losing worst patients
#> Stage III Net Loss 6 1.21457 448.4000000 448.4000000 0.0000000 0.00000 Minimal Minimal survival change from patient loss
#> Stage IV Net Gain 91 23.57513 253.1000000 254.8000000 1.7000000 0.67167 Minimal Minimal survival change from patient gain
#> Overall Assessment Mixed Pattern 313 14.90476 Strong Multiple stages show artificial survival improvement from patient reclassification
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Stage-Specific C-Index Analysis
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Stage N New System C-Index SE 95% CI Lower 95% CI Upper Prognostic Value
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage I 591 0.5688790 0.0144281 0.5405999 0.5971581 Poor discrimination (significant)
#> Stage II 629 0.5660672 0.0120072 0.5425330 0.5896013 Poor discrimination (significant)
#> Stage III 494 0.5280793 0.0115158 0.5055083 0.5506504 Poor discrimination (significant)
#> Stage IV 386 0.5090141 0.0038844 0.5014006 0.5166275 Poor discrimination (non-significant)
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Enhanced Pseudo R-squared Measures
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Measure Original System New System Improvement Relative Improvement (%) Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Nagelkerke R² 0.1052491 0.1398348 0.0345858 32.8608746 Substantial improvement
#> Cox & Snell R² 0.1052056 0.1397771 0.0345715 32.8608746 Substantial improvement
#> McFadden R² 0.0142637 0.0193197 0.0050559 35.4461537 Small improvement
#> Royston & Sauerbrei R² 0.0326846 0.0437630 0.0110784 33.8949220 Moderate improvement
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Enhanced Reclassification Metrics
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value 95% CI Lower 95% CI Upper p-value Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Category-free NRI 0.1173079 -0.0322051 0.2668209 0.1240937 Small improvement (category-free approach)
#> Clinical NRI (high-risk threshold) 0.0032447 -0.0604112 0.0669006 0.9204181 Minimal improvement (clinical thresholds)
#> Upstaging NRI 0.3333333 Substantial improvement (upstaged patients only)
#> Downstaging NRI 0.0000000 Minimal deterioration (downstaged patients only)
#> Weighted NRI (high-risk emphasis) -0.0180399 Minimal deterioration (risk-weighted approach)
#> Relative IDI (%) 32.1924689 3.9933987 60.3915390 < .0000001 Substantial improvement - not significant
#> Continuous NRI 0.0142099 -0.0616039 0.0900237 0.7133460 Minimal improvement (continuous risk scores)
#> Event Discrimination Improvement 0.0406682 0.0375952 0.0437412 < .0000001 Small improvement in event discrimination
#> Non-event Discrimination Improvement -0.0255993 -0.0286898 -0.0225088 < .0000001 Small deterioration in non-event discrimination
#> Kaplan-Meier based NRI 0.0000000 0.0000000 0.0000000 NaN Minimal deterioration (Kaplan-Meier based)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Proportional Hazards Assumption Test
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System Chi-Square df p-value Assumption Status Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System 0.2714032 3 0.9653139 Assumption Met Proportional hazards assumption is satisfied. Cox model is appropriate.
#> New Staging System 0.1487464 3 0.9854054 Assumption Met Proportional hazards assumption is satisfied. Cox model is appropriate.
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Decision Curve Analysis
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Time Point (months) Threshold (%) Net Benefit Original Net Benefit New Difference Clinical Impact Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 12.0000000 5.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 10.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 15.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 20.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 25.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 30.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 40.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 12.0000000 50.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 5.0000000 0.0038596 0.0035840 -0.0002757 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 10.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 15.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 20.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 25.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 30.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 40.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 24.0000000 50.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 5.0000000 0.0428822 0.0432581 0.0003759 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 10.0000000 0.0158730 0.0150794 -0.0007937 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 15.0000000 0.0045378 0.0024930 -0.0020448 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 20.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 25.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 30.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 40.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> 60.0000000 50.0000000 0.0000000 0.0000000 0.0000000 Minimal No clinically meaningful difference in net benefit
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #1976d2;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding the
#> Comparative Analysis Dashboard
#>
#> <p style="margin-bottom: 10px;">This dashboard provides an executive
#> summary of all stage migration analyses. It synthesizes complex
#> statistical results into actionable insights for clinical
#> decision-making.
#>
#> <h5 style="color: #34495e; margin-top: 15px;">Abbreviations and Terms
#> Explained:
#>
#> <ul style="margin-left: 20px;">
#> N/A (Not Applicable): This value is not relevant for the specific
#> metric. For example, "Total Patients" has no improvement value because
#> it's the same for both staging systems.
#> TBD (To Be Determined): The analysis is pending or requires you to
#> check the detailed analysis table mentioned in the recommendation
#> column. This appears when:
#>
#> Advanced analysis options need to be enabled
#> The specific analysis has not been run yet
#> The dashboard cannot automatically extract the value from detailed
#> results
#>
#>
#> C-Index: Concordance Index - measures discrimination ability (0.5 = no
#> discrimination, 1.0 = perfect discrimination)
#> CI: Confidence Interval - typically 95% CI unless otherwise specified
#> HR: Hazard Ratio - relative risk between stages
#> NRI: Net Reclassification Improvement - measures improvement in risk
#> classification
#>
#> *Category-Free NRI:* Uses continuous risk scores (most sensitive)
#> *Clinical NRI:* Uses clinically relevant risk thresholds
#> *Upstaging/Downstaging NRI:* Separate analysis by migration direction
#> *Weighted NRI:* Emphasizes high-risk patient classification (2x
#> weight)
#>
#>
#> IDI: Integrated Discrimination Improvement - measures improvement in
#> risk prediction
#> AUC: Area Under the Curve - discrimination measure for ROC analysis
#> PH: Proportional Hazards - assumption for Cox regression models
#> LR: Likelihood Ratio - model comparison statistic
#>
#>
#> <h5 style="color: #34495e; margin-top: 15px;">Column Definitions:
#>
#> <ul style="margin-left: 20px;">
#> Analysis Category: The type of analysis performed
#>
#> *Migration Overview:* Basic statistics about patient reclassification
#> *Discrimination:* Measures of model ability to distinguish risk levels
#> (C-index, AUC)
#> *Calibration:* Assessment of predicted vs observed survival
#> probabilities
#> *Reclassification:* Advanced NRI and IDI metrics including
#> category-specific and weighted approaches
#> *Model Fit:* Information criteria and likelihood-based model
#> comparison (AIC, BIC)
#> *Validation:* Checks for proper stage ordering and consistency
#> *Bias Assessment:* Detection of statistical artifacts or biases
#> *Model Assumptions:* Verification that statistical model requirements
#> are met
#> *Overall Assessment:* Synthesis of all analyses into final
#> recommendation
#>
#>
#> Metric: The specific measurement or test being reported
#> Original/New System: Values for the current and proposed staging
#> systems
#> Improvement: The change between systems (positive = improvement)
#> Statistical Significance: Whether the difference is statistically
#> meaningful
#> Clinical Relevance: Whether the difference matters in clinical
#> practice
#> Recommendation: Action-oriented guidance based on the results
#>
#>
#> <h5 style="color: #34495e; margin-top: 15px;">Key Metrics Explained:
#>
#> <ul style="margin-left: 20px;">
#> Migration Rate: Percentage of patients whose stage changed in the new
#> system. Higher rates indicate more substantial reclassification.
#> Monotonicity Score: Measures whether higher stages consistently have
#> worse survival (0-1 scale, 1 = perfect ordering)
#> Will Rogers Evidence: Detects if apparent improvements are due to
#> stage migration bias rather than true prognostic enhancement
#> Proportional Hazards: Checks if the staging system's predictive
#> ability remains constant over time
#>
#>
#> <h5 style="color: #34495e; margin-top: 15px;">Interpreting the Overall
#> Recommendation:
#>
#> <p style="margin-bottom: 5px;">The dashboard evaluates multiple
#> criteria and provides an evidence-based recommendation:
#>
#> <ul style="margin-left: 20px;">
#> "0/0 favorable": No positive indicators found among evaluated criteria
#> "Multiple Analyses": Several different statistical tests were
#> performed
#> "Critical Decision": The staging system choice has important clinical
#> implications
#> "Insufficient data": Not enough analyses completed for a definitive
#> recommendation
#>
#>
#> <h5 style="color: #34495e; margin-top: 15px;">How to Address TBD
#> Values:
#>
#> <p style="margin-bottom: 5px;">When you see "TBD" in the dashboard,
#> follow these steps:
#>
#> <ol style="margin-left: 20px;">
#> For Monotonicity Score: Enable "Stage Homogeneity Tests" or "Stage
#> Trend Analysis" options and rerun the analysis
#> For Will Rogers Evidence: The analysis should be available if
#> "Advanced Migration Analysis" is enabled - check the "Enhanced Will
#> Rogers Statistical Analysis" table
#> For Proportional Hazards: This is automatically tested - check the
#> "Proportional Hazards Assumption Testing" table
#> For other metrics: Enable the corresponding analysis option (e.g.,
#> "Calculate NRI", "Calculate IDI", "Perform ROC Analysis")
#>
#>
#> <p style="margin-top: 10px; font-style: italic; color: #7f8c8d;">
#> Note: For detailed results, refer to the specific analysis tables
#> mentioned in the recommendations.
#> The dashboard provides a high-level overview suitable for
#> presentations and decision-making, while the detailed
#> tables contain comprehensive statistical results for thorough
#> evaluation.
#>
#>
#>
#>
#>
#> Comparative Analysis Dashboard
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Analysis Category Metric Original System New System Improvement Statistical Significance Clinical Relevance Recommendation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Error Dashboard Generation Failed N/A N/A N/A N/A N/A Dashboard error: missing value where TRUE/FALSE needed
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> character(0)
#>
#> Will Rogers Phenomenon Evidence Summary
#> ──────────────────────────────────────────────────────────────────────────────────
#> Assessment Criterion Result Evidence Strength Clinical Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────
#> ──────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Will Rogers Analysis Clinical Recommendation
#> ──────────────────────────────────────────────────────────────────────
#> Category Finding Confidence Level Implementation Guidance
#> ──────────────────────────────────────────────────────────────────────
#> ──────────────────────────────────────────────────────────────────────
#>
#>
#> Enhanced Migration Pattern Analysis
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Pattern Type Count Percentage Flow Direction Clinical Impact
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Overall Migration 313 14.90000 Multi-directional Low impact: Stable staging criteria
#> Major Migration 66 11.20000 Stage I → Stage II Significant reclassification pattern
#> Major Migration 63 12.80000 Stage III → Stage IV Significant reclassification pattern
#> Stage Retention 496 83.90000 Remained in Stage I Stable stage definition
#> Stage Retention 512 81.40000 Remained in Stage II Stable stage definition
#> Stage Retention 400 81.00000 Remained in Stage III Stable stage definition
#> Stage Retention 379 98.20000 Remained in Stage IV Stable stage definition
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Landmark Analysis Results
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Landmark Time (months) N Patients N Events Original C-Index New C-Index C-Index Improvement Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Advanced Migration Heatmap Statistics
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage Retention Rate (%) Patients Gained Patients Lost Net Change Major Migration Flows
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Stage I 83.92555 30 95 -65 Stage I→Stage II (11.2%)
#> Stage II 81.39905 97 117 -20 Stage I→Stage II (11.2%)
#> Stage III 80.97166 88 94 -6 Stage III→Stage IV (12.8%)
#> Stage IV 98.18653 98 7 91 Stage III→Stage IV (12.8%)
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Error in `ggPalette()`:
#> ! Continuous value supplied to a discrete scale.
#> ℹ Example values: 496, 30, 0, 66, and 512.The Will Rogers analysis table provides a multi-criteria evidence assessment with a traffic-light grading system (PASS / BORDERLINE / CONCERN / FAIL). The visualization shows how survival curves shift within stages when patients are reclassified.
6. Bootstrap Validation
Internal validation using the bootstrap is essential for ensuring your findings are not over-optimistic. The module performs optimism-corrected estimates of the C-index difference, using Harrell’s recommended bootstrap procedure.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
performBootstrap = TRUE,
bootstrapReps = 100,
useOptimismCorrection = TRUE,
showStatisticalComparison = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Statistical Comparison
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value 95% CI Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging C-index 0.6258 [0.6096, 0.6420] Fair discrimination
#> New Staging C-index 0.6452 [0.6295, 0.6610] Fair discrimination
#> C-index Improvement +0.0194 [-0.0032, +0.0420] Small improvement
#> Relative Improvement +3.1% N/A Moderate
#> AIC Difference (Δ) 82.75 N/A Strong evidence for new model
#> BIC Difference (Δ) 82.75 N/A Very strong evidence
#> Clinical Significance No Threshold: 0.020 Below clinical threshold
#> Overall Recommendation 3/4 criteria met N/A Recommended for adoption
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Bootstrap Validation Results
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Apparent Bootstrap Mean Bootstrap SE 95% CI Lower 95% CI Upper Optimism Optimism Corrected Success Rate Clinical Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> C-index Improvement . . . . . -0.0002811 . . .
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> character(0)
#>
#> LR Chi-Square Comparison (Key Staging Validation Metric)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System LR Chi-Square df p-value Goodness of Fit Model Quality
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. LR Chi-Square measures model goodness-of-fit vs null model. Higher values indicate better prognostic
#> discrimination. This is a key metric for staging validation.With 100 bootstrap repetitions (use 1000 for publications), the output includes:
- Apparent C-index difference (what you see in the data)
- Optimism estimate (how much the apparent estimate is inflated)
- Optimism-corrected C-index difference (the honest estimate)
- Bootstrap confidence intervals
If the optimism-corrected estimate remains above your clinical significance threshold, you have robust evidence that the new staging system is genuinely better.
7. Decision Curve Analysis (DCA)
Decision Curve Analysis moves beyond discrimination to clinical utility. It asks: At what range of decision thresholds does using the new staging system lead to better clinical decisions than treating all patients, treating no patients, or using the old staging system?
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
performDCA = TRUE,
showDecisionCurves = TRUE,
showClinicalInterpretation = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Decision Curve Analysis
#> ───────────────────────────────────────────────────────────────────────────
#> Threshold Net Benefit (Original) Net Benefit (New) Improvement
#> ───────────────────────────────────────────────────────────────────────────
#> 0.1000000 0.0158730 0.0150794 -0.0007937
#> 0.2000000 0.0000000 0.0000000 0.0000000
#> 0.3000000 0.0000000 0.0000000 0.0000000
#> 0.4000000 0.0000000 0.0000000 0.0000000
#> 0.5000000 0.0000000 0.0000000 0.0000000
#> 0.6000000 0.0000000 0.0000000 0.0000000
#> 0.7000000 0.0000000 0.0000000 0.0000000
#> 0.8000000 0.0000000 0.0000000 0.0000000
#> 0.9000000 0.0000000 0.0000000 0.0000000
#> ───────────────────────────────────────────────────────────────────────────
#> Note. Analysis performed at 60 months
#> Note. Models compared: 'old_risk' vs 'new_risk'
#> Note. Successfully extracted 396 data points from DCA object
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f3e5f5; border-left: 4px solid #9c27b0;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Decision
#> Curve Analysis
#>
#> <p style="margin-bottom: 10px;">Decision curves help determine when
#> using a staging system provides clinical benefit:
#>
#> <ul style="margin-left: 20px;">
#> X-axis: Threshold probability (risk tolerance)
#> Y-axis: Net benefit (clinical utility)
#> Gray line: Treat all patients (assume everyone has high risk)
#> Black line: Treat no patients (assume everyone has low risk)
#> Colored lines: Staging system performance
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Higher curves indicate better clinical utility
#> Curves above "treat all" and "treat none" lines show clinical benefit
#> The range of thresholds where curves are highest indicates optimal use
#> Compare staging systems across different risk thresholds
#> Helps inform treatment decisions based on acceptable risk levels
#>
#>
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #e8f5e8; border-left: 4px solid #4caf50;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Clinical
#> Interpretation Guide
#>
#> <p style="margin-bottom: 10px;">This table provides evidence-based
#> recommendations for staging system adoption:
#>
#> <ul style="margin-left: 20px;">
#> Metric: Statistical measure being evaluated
#> Value: Actual numerical result with magnitude assessment
#> Interpretation: Clinical significance classification
#> Recommendation: Evidence-based guidance for implementation
#>
#> <p style="margin-bottom: 5px;">Recommendation categories:
#>
#> <ul style="margin-left: 20px;">
#> RECOMMEND ADOPTION: Strong evidence for clinical benefit
#> CONSIDER ADOPTION: Moderate evidence, further validation suggested
#> INSUFFICIENT EVIDENCE: Statistical significance without clinical
#> meaning
#> DO NOT ADOPT: No meaningful improvement demonstrated
#>
#>
#>
#>
#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value Interpretation Recommendation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> C-index Improvement +0.019 (3.1%) Magnitude: negligible .
#> Significance p = 0.31016 Neither statistically nor clinically significant Strength: None
#> Recommendation DO NOT ADOPT New staging system does not provide meaningful improvement over existing system. Confidence: High
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
The decision curve plot shows net benefit on the y-axis across threshold probabilities on the x-axis. The new staging system is clinically useful at thresholds where its curve lies above both the “treat all” and “treat none” reference lines, and ideally above the old staging system curve.
8. Calibration Analysis
Calibration assesses whether the predicted survival probabilities from the staging system match observed outcomes. A well-calibrated staging system not only ranks patients correctly (discrimination) but also assigns accurate absolute risk estimates.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
performCalibration = TRUE,
showCalibrationPlots = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #fff3e0; border-left: 4px solid #ff9800;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Enhanced
#> Calibration Analysis
#>
#> <p style="margin-bottom: 10px;">Comprehensive calibration analysis
#> assesses how well predicted survival probabilities match observed
#> outcomes using both traditional and advanced spline-based methods:
#>
#> <div style="margin-bottom: 15px;">
#> <h5 style="color: #d84315; margin-bottom: 8px;">Traditional Linear
#> Methods:
#>
#> <ul style="margin-left: 20px;">
#> Hosmer-Lemeshow Test: Tests goodness-of-fit for survival models (p
#> >0.05 = well-calibrated)
#> Calibration Slope: Linear slope of predicted vs observed probabilities
#> (ideal = 1.0)
#> Calibration Intercept: Intercept of linear calibration line (ideal =
#> 0.0)
#> 95% CI: Confidence intervals for calibration slope
#>
#>
#> <div style="margin-bottom: 15px;">
#> <h5 style="color: #2e7d32; margin-bottom: 8px;">Advanced Spline
#> Methods:
#>
#> <ul style="margin-left: 20px;">
#> Spline Calibration: Uses Restricted Cubic Splines (RCS) for flexible
#> non-linear calibration assessment
#> Enhanced Detection: Identifies calibration patterns that linear
#> methods cannot capture
#> Robust Assessment: Provides calibration slope/intercept estimates
#> accounting for non-linearity
#>
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Traditional: Well-calibrated model has H-L p >0.05, slope ≈ 1.0,
#> intercept ≈ 0.0
#> Spline: H-L test not applicable; focus on spline slope and visual
#> calibration plots
#> Over-prediction: Slope <1.0 (predictions too high)
#> Under-prediction: Slope >1.0 (predictions too low)
#> Systematic bias: Intercept significantly different from 0
#> Non-linear patterns: Spline methods detect complex calibration issues
#> across probability ranges
#>
#>
#>
#>
#> Calibration Analysis
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model H-L Chi² H-L df H-L p-value Calibration Slope Calibration Intercept Slope 95% CI Lower Slope 95% CI Upper Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging 11.2454620 2 0.0036148 2.8553312 -1.4654809 2.3030060 3.4143152 H-L test: poor fit; Under-prediction (slope > 1.2); Good overall calibration
#> New Staging 22.0461578 2 0.0000163 2.8840685 -1.5722649 2.4042375 3.3701696 H-L test: poor fit; Under-prediction (slope > 1.2); Systematic over-prediction
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #fff3e0; border-left: 4px solid #ff9800;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Enhanced
#> Calibration Plots
#>
#> <p style="margin-bottom: 10px;">Enhanced calibration plots provide
#> comprehensive visual assessment of how well predicted survival
#> probabilities match observed outcomes using dual-curve methodology:
#>
#> <div style="margin-bottom: 15px;">
#> <h5 style="color: #d84315; margin-bottom: 8px;">Plot Components:
#>
#> <ul style="margin-left: 20px;">
#> X-axis: Predicted survival probability from Cox model
#> Y-axis: Observed survival probability from data
#> Gray diagonal line: Perfect calibration reference (predicted =
#> observed)
#> Data points: Binned predicted vs observed probabilities
#> Separate plots: Original vs New staging systems side-by-side
#>
#>
#> <div style="margin-bottom: 15px;">
#> <h5 style="color: #2e7d32; margin-bottom: 8px;">Dual Calibration
#> Curves:
#>
#> <ul style="margin-left: 20px;">
#> Loess curve (solid): Traditional smooth calibration curve with
#> confidence bands
#> Spline curve (dashed, green): Flexible GAM-based calibration using
#> restricted cubic splines
#> Enhanced detection: Spline curves reveal non-linear calibration
#> patterns
#> Confidence bands: Statistical uncertainty for both curve types
#>
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Perfect calibration: Both curves closely follow the diagonal line
#> Systematic patterns: Curves consistently above/below diagonal indicate
#> bias
#> Non-linear calibration: Spline curves reveal complex calibration
#> issues
#> Curve agreement: Similar Loess and spline curves suggest robust
#> calibration
#> Staging comparison: Compare calibration quality between original and
#> new systems
#> Clinical utility: Better calibrated models provide more accurate risk
#> predictions
#> TableGrob (3 x 2) "arrange": 4 grobs
#> z cells name grob
#> 1 1 (2-2,1-1) arrange gtable[layout]
#> 2 2 (2-2,2-2) arrange gtable[layout]
#> 3 3 (1-1,1-2) arrange text[GRID.text.258]
#> 4 4 (3-3,1-2) arrange text[GRID.text.259]The calibration plots compare predicted vs. observed survival. Points lying on the 45-degree line indicate perfect calibration. Systematic deviation above the line means the model overestimates survival; below the line means it underestimates.
9. Survival Curves
Kaplan-Meier curves stratified by stage are the most intuitive way to visualize staging system performance. Well-separated curves with no crossing indicate good prognostic discrimination.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "standard",
showSurvivalCurves = TRUE,
survivalPlotType = "separate",
showConfidenceIntervals = TRUE,
showRiskTables = TRUE,
showForestPlot = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ed; border-left: 4px solid #4caf50;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Hazard Ratio
#> Forest Plots
#>
#> <p style="margin-bottom: 10px;">Forest plots display hazard ratios
#> (HR) with confidence intervals for each stage:
#>
#> <ul style="margin-left: 20px;">
#> X-axis: Hazard Ratio (log scale)
#> Y-axis: Stage categories for each staging system
#> Points: Hazard ratio estimates
#> Horizontal lines: 95% confidence intervals
#> Vertical red line: HR = 1.0 (no effect)
#>
#> <p style="margin-bottom: 5px;">Interpretation:
#>
#> <ul style="margin-left: 20px;">
#> HR = 1.0: No increased risk
#> HR > 1.0: Increased risk of event
#> HR < 1.0: Decreased risk of event
#> Confidence intervals not crossing 1.0 indicate statistical
#> significance
#> * p<0.05, ** p<0.01, *** p<0.001
#> Compare HR patterns between staging systems
#>
#>
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #e8f5e8; border-left: 4px solid #4caf50;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Survival
#> Curves Comparison
#>
#> <p style="margin-bottom: 10px;">Survival curves show the probability
#> of event-free survival over time for each stage:
#>
#> <ul style="margin-left: 20px;">
#> X-axis: Time (months or years)
#> Y-axis: Survival probability (0 to 1)
#> Different colors: Different stages within each system
#> Left panel: Original staging system
#> Right panel: New staging system
#> Shaded areas: Confidence intervals (if enabled)
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Curves should be well-separated (good discrimination)
#> Higher stages should have lower survival curves
#> Non-crossing curves indicate consistent prognostic order
#> Compare separation between systems - better separation = better
#> staging
#> Risk tables (if enabled) show number of patients at risk over time
Available survivalPlotType options:
- separate: Individual KM plots for old and new staging systems
- sidebyside: Old and new systems plotted side by side for visual comparison
- overlay: Both systems overlaid on the same axes
The forest plot shows stage-specific hazard ratios with confidence intervals, making it easy to compare the magnitude of between-stage separation in each system.
10. Clinical Presets
For users who do not want to manually configure dozens of options, the module provides clinical presets that activate sensible combinations:
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
clinicalPreset = "research_study"
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'research_study'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────| Preset | What It Enables | Best For |
|---|---|---|
routine_clinical |
Migration matrix, C-index, basic recommendation | Daily clinical validation |
research_study |
+ NRI, survival curves, Will Rogers, bootstrap | Academic research projects |
publication_ready |
All methods + all visualizations | Manuscript preparation |
custom |
Manual control of every option | Advanced users |
The complexityMode option works similarly but controls
UI complexity in the jamovi interface:
- quick: Essential outputs only (5-10 min analysis)
- standard: Common validation metrics (30-60 min)
- comprehensive: All methods enabled (1-2 hours for large datasets)
- custom: Full manual control
11. Cancer-Type-Specific Analysis
Different cancer types have different expected migration patterns and clinically meaningful thresholds. Specifying the cancer type adjusts interpretation guidelines.
stagemigration(
data = lung_df,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
cancerType = "lung",
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE,
showClinicalInterpretation = TRUE,
showExplanations = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ──────────────────────────────────────────────
#> Statistic Value Percentage
#> ──────────────────────────────────────────────
#> Total Patients 700.00000 100%
#> Unchanged Stage 579.00000 82.7%
#> Migrated Stage 121.00000 17.3%
#> Upstaged 96.00000 13.7%
#> Downstaged 25.00000 3.6%
#> ──────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 700 patients with 392
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 163 25 9 0 197
#> Stage II 13 175 22 11 221
#> Stage III 0 11 127 29 167
#> Stage IV 0 0 1 114 115
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #e8f5e8; border-left: 4px solid #4caf50;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Clinical
#> Interpretation Guide
#>
#> <p style="margin-bottom: 10px;">This table provides evidence-based
#> recommendations for staging system adoption:
#>
#> <ul style="margin-left: 20px;">
#> Metric: Statistical measure being evaluated
#> Value: Actual numerical result with magnitude assessment
#> Interpretation: Clinical significance classification
#> Recommendation: Evidence-based guidance for implementation
#>
#> <p style="margin-bottom: 5px;">Recommendation categories:
#>
#> <ul style="margin-left: 20px;">
#> RECOMMEND ADOPTION: Strong evidence for clinical benefit
#> CONSIDER ADOPTION: Moderate evidence, further validation suggested
#> INSUFFICIENT EVIDENCE: Statistical significance without clinical
#> meaning
#> DO NOT ADOPT: No meaningful improvement demonstrated
#>
#>
#>
#>
#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value Interpretation Recommendation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> C-index Improvement +0.015 (2.4%) Magnitude: negligible .
#> Significance p = 0.26067 Neither statistically nor clinically significant Strength: None
#> Recommendation DO NOT ADOPT New staging system does not provide meaningful improvement over existing system. Confidence: High
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Supported cancer types: general, lung,
breast, colorectal, prostate,
headneck, melanoma, other. Each
adjusts thresholds and interpretation text.
12. Competing Risks Analysis
In many oncology settings, patients face multiple possible events - cancer-specific death and death from other causes. Standard survival analysis treats competing events as censored, which can bias estimates. The competing risks framework uses Fine-Gray subdistribution hazard models and Cumulative Incidence Functions (CIF) to properly handle this.
# Add a competing event variable for demonstration
cr_data <- combined_data
set.seed(42)
cr_data$event_type <- ifelse(
cr_data$event == 1,
sample(c("cancer_death", "other_death"), sum(cr_data$event == 1),
replace = TRUE, prob = c(0.75, 0.25)),
"censored"
)
cr_data$event_type <- factor(cr_data$event_type)
stagemigration(
data = cr_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
performCompetingRisks = TRUE,
competingEventVar = "event_type",
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> character(0)
#>
#> Competing Risks Event Distribution by Stage
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System Stage N Total N Primary N Competing N Censored Primary Rate Competing Rate
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Competing Risks Analysis Comparison
#> ────────────────────────────────────────────────────────────────────────
#> System Metric Primary Events Competing Events Assessment
#> ────────────────────────────────────────────────────────────────────────
#> ────────────────────────────────────────────────────────────────────────The competing risks analysis provides:
- Cumulative incidence functions for each event type
- Fine-Gray subdistribution hazard ratios comparing staging systems
- Gray’s test for equality of CIF across stages
13. Random Survival Forests [Experimental]
For a non-parametric comparison that makes no assumptions about proportional hazards, the module can fit random survival forests (RSF) to both staging systems and compare variable importance and prediction accuracy.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
performRandomForestAnalysis = TRUE,
forestModelType = "rsf",
forestNTrees = 200,
calculateVariableImportance = TRUE,
forestDiscriminationMetrics = TRUE,
forestStagingComparison = TRUE,
showMigrationOverview = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Random Forest Variable Importance
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Variable Importance Score Rank Permutation p-value Importance Type Variable Type Clinical Relevance Staging Contribution
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Random Forest Model Performance
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model Type Staging System C-Index C-Index Lower CI C-Index Upper CI OOB Error Rate Integrated Brier Score Model Complexity Performance Grade
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Random Forest vs Cox Model Comparison
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Performance Metric Cox Original Cox New Forest Original Forest New Best Method Improvement Significance Clinical Impact
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Forest-Based Staging System Comparison
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Analysis Component Original System New System Forest Assessment Improvement Statistical Evidence Clinical Recommendation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────RSF analysis is computationally expensive. For exploratory work, use
forestNTrees = 200; for publications, use 500-1000. The
output includes:
- Variable importance rankings for each staging system
- C-index from RSF models vs. Cox models
- Comparison of staging systems in the non-parametric framework
14. Advanced Features
Homogeneity and Trend Tests
These tests verify that the staging system satisfies the fundamental requirements: patients within the same stage should have similar prognosis (homogeneity), and higher stages should consistently have worse prognosis (monotonic trend).
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
performHomogeneityTests = TRUE,
performTrendTests = TRUE,
performLikelihoodTests = TRUE,
calculatePseudoR2 = TRUE,
showStatisticalSummary = TRUE,
showMethodologyNotes = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Pseudo R-squared Measures
#> ────────────────────────────────────────────────────────────────────────────────────────────
#> Measure Original System New System Improvement Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────
#> Nagelkerke R² 0.1052491 0.1398348 0.0345858 Weak fit
#> McFadden R² 0.0142637 0.0193197 0.0050559 Weak fit
#> Cox-Snell R² 0.1052056 0.1397771 0.0345715 Weak fit
#> Adjusted McFadden R² 0.0138971 0.0189531 0.0050559 Weak fit
#> Royston & Sauerbrei R² Not available
#> ────────────────────────────────────────────────────────────────────────────────────────────
#> Note. Interpretation: Higher values indicate better model fit. Positive improvement
#> values favor the new staging system.
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #2196f3;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Likelihood
#> Ratio Tests
#>
#> <p style="margin-bottom: 10px;">Likelihood ratio tests compare the
#> goodness-of-fit between nested Cox models to assess if the new staging
#> system provides significantly better survival prediction:
#>
#> <ul style="margin-left: 20px;">
#> Chi-Square Statistic: Measures the difference in log-likelihoods
#> between models (higher = more difference)
#> Degrees of Freedom (df): Difference in the number of parameters
#> between models
#> P-value: Statistical significance of the improvement (p < 0.05 =
#> significant improvement)
#>
#> <p style="margin-bottom: 10px;">Interpretation:
#>
#> <ul style="margin-left: 20px;">
#> df = 0: Models have same complexity; comparison limited (often occurs
#> when staging systems have same number of categories)
#> df > 0: New system is more complex; test evaluates if added complexity
#> improves fit significantly
#> p < 0.05: New staging system provides statistically significant
#> improvement in survival prediction
#> p >= 0.05: No significant improvement; simpler (original) model may be
#> preferred
#>
#> <p style="margin-bottom: 0; font-style: italic; color: #666;">Note:
#> When df=0, focus on other metrics like C-index difference and clinical
#> significance rather than p-value.
#>
#>
#>
#>
#> Likelihood Ratio Tests
#> ───────────────────────────────────────────────────────────
#> Test Chi-Square df p-value
#> ───────────────────────────────────────────────────────────
#> Likelihood Ratio Test -82.7450925 3 0.3101605
#> ───────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #2196f3;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Linear Trend
#> Chi-square Tests
#>
#> <p style="margin-bottom: 10px;">Linear trend tests assess whether
#> there is a systematic increase in hazard across ordered stages:
#>
#> <ul style="margin-left: 20px;">
#> Wald Chi-Square: Tests linear trend in log-hazard across stages
#> (higher = stronger trend)
#> P-value: Statistical significance of the linear trend (p < 0.05 =
#> significant trend)
#> Coefficient: Direction and magnitude of trend (positive = increasing
#> hazard with higher stages)
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Significant trends indicate proper stage ordering with prognostic
#> value
#> Non-significant trends may suggest stage grouping issues or
#> insufficient sample size
#> Compare trends between staging systems to assess improvement in
#> ordinal ranking
#>
#>
#>
#>
#> Linear Trend Chi-square Tests
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System Wald Chi-Square df p-value Coefficient # Stages Interpretation
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging 234.3900000 1 < .0000001 Unable to interpret
#> New Staging 308.5600000 1 < .0000001 Unable to interpret
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. Linear trend tests assess ordinal progression in survival risk across stages. Significant trends
#> indicate proper stage ordering.
#>
#>
#> Stage Homogeneity Tests
#> ────────────────────────────────────────────────────────────────────────────────
#> Stage Test Statistic p-value
#> ────────────────────────────────────────────────────────────────────────────────
#> Original Staging Overall (Log-rank) 249.6559745 < .0000001
#> Original Staging Trend Test (Cox) 15.3097371 < .0000001
#> Original Stage I Within-Stage Homogeneity 950.5094541 < .0000001
#> Original Stage II Within-Stage Homogeneity 1110.7959523 < .0000001
#> Original Stage III Within-Stage Homogeneity 982.9226025 < .0000001
#> Original Stage IV Within-Stage Homogeneity 764.2242901 < .0000001
#> Original Staging Jonckheere-Terpstra 1.7320508 < .0000001
#> Original Staging Separation Test 0.7064323 0.4934014
#> New Staging Overall (Log-rank) 333.7840725 < .0000001
#> New Staging Trend Test (Cox) 17.5657474 < .0000001
#> New Stage I Within-Stage Homogeneity 705.1574608 < .0000001
#> New Stage II Within-Stage Homogeneity 1070.8118186 < .0000001
#> New Stage III Within-Stage Homogeneity 968.0950059 < .0000001
#> New Stage IV Within-Stage Homogeneity 947.4383968 < .0000001
#> New Staging Jonckheere-Terpstra 1.7320508 < .0000001
#> New Staging Separation Test 0.8656494 0.4207782
#> ────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Stage Trend Analysis
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System Test Statistic p-value Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System Cox Trend Test 15.3097371 < .0000001 Significant positive trend (higher stages = worse survival)
#> New Staging System Cox Trend Test 17.5657474 < .0000001 Significant positive trend (higher stages = worse survival)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #e3f2fd; border-left: 4px solid #2196f3;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding the
#> Statistical Summary
#>
#> <p style="margin-bottom: 10px;">This table consolidates all
#> statistical tests and measures in one comprehensive view:
#>
#> <ul style="margin-left: 20px;">
#> Method: Statistical test or measure performed
#> Result: Numerical value of the test statistic or measure
#> 95% CI: Confidence interval when available
#> p-value: Statistical significance level
#> Significance: Whether the result is statistically significant
#>
#> <p style="margin-bottom: 5px;">Use this table to:
#>
#> <ul style="margin-left: 20px;">
#> Review all statistical results in one location
#> Identify which measures show statistical significance
#> Support comprehensive peer review and publication
#> Cross-reference with clinical interpretation
#>
#>
#>
#>
#> Statistical Summary
#> ────────────────────────────────────────────────────────────────────────────────────────────────────
#> Method Result 95% CI p-value Significance
#> ────────────────────────────────────────────────────────────────────────────────────────────────────
#> C-index Improvement 0.0194 [-0.0341, +0.0698] 0.5010000 No
#> AIC Difference (Δ) 8.05 N/A Moderate evidence
#> BIC Difference (Δ) 8.05 N/A Strong evidence
#> Relative Improvement +3.1% N/A Moderate
#> Overall Assessment 3/4 criteria met N/A Recommended
#> ────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f5f5f5; border-left: 4px solid #333;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Statistical Methodology
#>
#>
#>
#> Concordance Index (C-Index)
#>
#> The concordance index measures the probability that, for any randomly
#> selected pair of patients, the patient with the worse predicted
#> outcome (higher stage) actually experienced the event sooner. Values
#> range from 0.5 (no discrimination) to 1.0 (perfect discrimination).
#>
#>
#>
#> Net Reclassification Improvement (NRI)
#>
#> NRI quantifies the net proportion of patients correctly reclassified
#> by the new staging system. It separately considers improvements in
#> classification for patients who experienced events (NRI+) and those
#> who did not (NRI-).
#>
#>
#>
#> Integrated Discrimination Improvement (IDI)
#>
#> IDI measures the improvement in average sensitivity minus the decrease
#> in average specificity. It represents the improvement in model
#> discrimination on a continuous scale.
#>
#>
#>
#> Time-dependent ROC Analysis
#>
#> ROC curves at specific time points assess the staging systems' ability
#> to discriminate between patients who will experience events before
#> that time versus those who will not.
#>
#>
#>
#> Bootstrap Validation
#>
#> Bootstrap resampling provides internal validation and
#> optimism-corrected performance estimates. The optimism is calculated
#> as the difference between apparent and bootstrap performance.
#>
#>
#>
#> Model Comparison
#>
#> AIC and BIC differences quantify the relative quality of models, with
#> lower values indicating better fit. Differences >4 suggest moderate
#> evidence, >10 strong evidence for the better model.
#>
#>
#>
#> Clinical Significance
#>
#> Statistical significance does not always imply clinical relevance. We
#> use established thresholds: C-index improvement >0.02 and NRI >0.20 to
#> determine clinically meaningful improvements.
#>
#>
#>
#> Enhanced Reclassification Metrics
#>
#> Multiple NRI approaches provide comprehensive reclassification
#> assessment:
#>
#>
#> Category-Free NRI: Uses continuous risk scores - most sensitive to
#> subtle improvements
#> Clinical NRI: Based on clinically relevant thresholds (e.g., top
#> tertile = high-risk)
#> Category-Specific NRI: Separate evaluation for upstaged vs downstaged
#> patients
#> Weighted NRI: Emphasizes correct classification of high-risk patients
#> (2.0x weight vs 1.0x for low-risk)
#>
#>
#>
#> These complementary approaches capture different aspects of
#> reclassification quality, providing a comprehensive evaluation of
#> staging system improvements.Multifactorial Analysis
When other prognostic variables (age, grade, biomarkers) are available, the multifactorial analysis adjusts the staging comparison for these confounders.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
enableMultifactorialAnalysis = TRUE,
continuousCovariates = "age",
categoricalCovariates = "sex",
multifactorialComparisonType = "comprehensive",
showMultifactorialTables = TRUE,
showAdjustedCIndexComparison = TRUE,
showNestedModelTests = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #4169e1;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Advanced Multifactorial
#> Stage Migration Analysis
#>
#> <p style="margin-bottom: 15px;">This comprehensive analysis evaluates
#> staging system performance using state-of-the-art multivariable
#> methods, accounting for other prognostic factors and providing
#> clinically actionable insights.
#>
#> <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px;
#> margin-bottom: 15px;">
#>
#> <h5 style="color: #1976d2; margin-bottom: 8px;">Core Analyses
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 14px;">
#> Adjusted C-index: Discriminative ability after covariate adjustment
#> Nested Model Tests: Likelihood ratio tests comparing staging systems
#> Bootstrap Model Selection: Stability assessment with 500 bootstrap
#> samples
#> Advanced Interaction Detection: Stage-covariate interaction testing
#> Comprehensive Model Diagnostics: Validation and performance metrics
#>
#>
#>
#> <h5 style="color: #1976d2; margin-bottom: 8px;">Advanced Methods
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 14px;">
#> Adjusted NRI: Net reclassification improvement with covariates
#> Multivariable Decision Curves: Clinical utility across models
#> Personalized Predictions: Individual patient risk assessments
#> Risk Profiles: Representative patient archetypes
#> Clinical Recommendations: Automated treatment intensity guidance
#>
#>
#>
#>
#> <div style="background-color: #fff; padding: 12px; border-radius: 4px;
#> margin-bottom: 15px;">
#> <h5 style="color: #d32f2f; margin-bottom: 8px;">Clinical Significance
#> Thresholds
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 14px;">
#> C-index improvement >= 0.02 (clinically meaningful discrimination
#> gain)
#> NRI >= 20% (substantial reclassification improvement)
#> Bootstrap selection frequency > 80% (high stability variables)
#> Risk difference > 10% (significant individual impact)
#>
#>
#>
#> <div style="background-color: #e8f5e8; padding: 12px; border-radius:
#> 4px;">
#> <h5 style="color: #2e7d32; margin-bottom: 8px;">Clinical Applications
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 14px;">
#> Evidence-based adoption: Robust statistical evidence for staging
#> system changes
#> Real-world performance: Accounts for confounding by other prognostic
#> factors
#> Personalized medicine: Individual patient risk assessments and
#> recommendations
#> Subgroup analysis: Identifies patient populations with greatest
#> benefit
#> Decision support: Net benefit analysis for treatment threshold
#> decisions
#>
#>
#>
#> <div style="background-color: #fff3e0; padding: 12px; border-radius:
#> 4px; margin-top: 15px;">
#> <h5 style="color: #e65100; margin-bottom: 8px;">Configuration Guidance
#> & Resource Estimation
#>
#> <div style="display: grid; grid-template-columns: 1fr 1fr; gap:
#> 15px;">
#>
#> <p style="margin: 0 0 8px 0; font-size: 13px; font-weight: bold;
#> color: #d84315;">Comparison Types:
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 13px;">
#> Comprehensive: High-impact research (15-30 min)
#> Adjusted C-index: Limited resources (2-5 min)
#> Nested models: Formal testing (5-10 min)
#> Stepwise: Variable selection (3-8 min)
#>
#>
#>
#> <p style="margin: 0 0 8px 0; font-size: 13px; font-weight: bold;
#> color: #d84315;">Sample Size Guidelines:
#>
#> <ul style="margin: 0; padding-left: 16px; font-size: 13px;">
#> < 500 patients: All methods feasible
#> 500-2000: Monitor bootstrap operations
#> > 2000: Consider reducing iterations
#> > 10000: Use standard analysis
#>
#>
#>
#>
#>
#> <p style="margin-top: 15px; margin-bottom: 0; font-style: italic;
#> color: #666; font-size: 13px;">
#> Note: This analysis represents the current state-of-the-art in staging
#> system validation,
#> incorporating methods from recent oncology and biostatistics
#> literature for comprehensive evaluation
#> of prognostic model improvements in multivariable settings. See
#> stagemigration_analysis_guide.md for
#> detailed configuration selection guidance based on your research
#> context.
#>
#>
#>
#>
#>
#> Multifactorial Model Results
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model C-Index SE 95% CI Lower 95% CI Upper AIC BIC
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Baseline (Covariates Only) 0.5047590 0.0092696 0.4865906 0.5229274 16369.4699270 16379.5736383
#> Original Staging + Covariates 0.6274275 0.0085568 0.6106562 0.6441989 16141.9683049 16167.2275830
#> New Staging + Covariates 0.6471058 0.0083008 0.6308362 0.6633755 16059.1927333 16084.4520114
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. Multifactorial analysis included 2 covariates with 2100 patients after complete case analysis.
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #4169e1;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding
#> Multifactorial Model Results
#>
#> <p style="margin-bottom: 10px;">This table compares the performance of
#> different models that combine staging systems with covariates:
#>
#> <ul style="margin-left: 20px;">
#> Model: The specific combination of staging system and covariates
#> C-Index: Concordance index (discrimination ability) of the model
#> SE: Standard error of the C-index estimate
#> 95% CI: Confidence interval for the C-index
#> AIC: Akaike Information Criterion (lower is better)
#> BIC: Bayesian Information Criterion (lower is better)
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Compare C-index values to assess discrimination improvement
#> Lower AIC/BIC values indicate better model fit
#> Models with overlapping confidence intervals may not be significantly
#> different
#> Choose the model that balances discrimination with simplicity
#>
#>
#>
#>
#> Adjusted C-Index Comparison (Multifactorial)
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Comparison C-Index Difference SE 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Baseline vs Original + Covariates 0.1226685 0.0126152 0.0979427 0.1473944 < .0000001
#> Baseline vs New + Covariates 0.1423468 0.0124431 0.1179584 0.1667352 < .0000001
#> Original + Covariates vs New + Covariates 0.0196783 0.0119215 -0.0036879 0.0430445 0.0988098
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #4169e1;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Adjusted
#> C-Index Comparison
#>
#> <p style="margin-bottom: 10px;">This table compares the discriminative
#> ability (C-index) of models adjusted for covariates:
#>
#> <ul style="margin-left: 20px;">
#> Comparison: Specific model comparison being evaluated
#> C-Index Difference: Difference in discrimination between models
#> SE: Standard error of the difference estimate
#> 95% CI: Confidence interval for the difference
#> p-value: Statistical significance of the improvement
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Positive differences indicate improvement in the new staging system
#> Differences >0.05 are generally considered clinically meaningful
#> p-values <0.05 indicate statistically significant improvements
#> Consider both statistical significance and clinical relevance
#>
#>
#>
#>
#> Nested Model Tests
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Model Comparison Chi-Square df p-value Decision
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging vs Covariates Only 233.5016221 3 < .0000001 Highly significant improvement
#> New Staging vs Covariates Only 316.2771937 3 < .0000001 Highly significant improvement
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #f0f8ff; border-left: 4px solid #4169e1;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding Nested Model
#> Tests
#>
#> <p style="margin-bottom: 10px;">These likelihood ratio tests compare
#> nested models to assess if adding variables significantly improves
#> model fit:
#>
#> <ul style="margin-left: 20px;">
#> Model Comparison: Specific models being compared (simpler vs. more
#> complex)
#> Chi-Square: Test statistic measuring improvement in model fit
#> df: Degrees of freedom (difference in parameters between models)
#> p-value: Statistical significance of the improvement
#> Decision: Interpretation of the statistical result
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> Significant p-values indicate the more complex model fits
#> significantly better
#> Non-significant results suggest the simpler model is adequate
#> Balance model complexity with clinical interpretability
#> Consider effect sizes alongside statistical significanceTime-Dependent ROC Analysis
Compares the discriminative ability of staging systems at specific time points. This is especially useful when staging performance changes over time (e.g., a staging system may discriminate well at 1 year but poorly at 5 years).
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
performROCAnalysis = TRUE,
rocTimePoints = "12, 24, 60",
showROCComparison = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Time-dependent ROC Analysis
#> ─────────────────────────────────────────────────────────────────────────────────
#> Time Point (months) AUC (Original) AUC (New) Difference p-value
#> ─────────────────────────────────────────────────────────────────────────────────
#> 12.00000 0.6410641 0.6274255 -0.0136386 0.4993831
#> 24.00000 0.6496866 0.6546512 0.0049646 0.7597600
#> 60.00000 0.6530169 0.6515822 -0.0014347 0.8875026
#> ─────────────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #e8f4fd; border-left: 4px solid #2196f3;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Understanding
#> Time-dependent ROC Curves
#>
#> <p style="margin-bottom: 10px;">ROC curves show the discriminative
#> ability of staging systems at specific time points:
#>
#> <ul style="margin-left: 20px;">
#> X-axis (FPR): False Positive Rate (1 - Specificity)
#> Y-axis (TPR): True Positive Rate (Sensitivity)
#> Diagonal line: Random classification (AUC = 0.5)
#> Curves closer to top-left: Better discrimination
#> AUC values: Area under the curve (0.5 = random, 1.0 = perfect)
#>
#> <p style="margin-bottom: 5px;">Clinical interpretation:
#>
#> <ul style="margin-left: 20px;">
#> AUC 0.5-0.6: Poor discrimination
#> AUC 0.6-0.7: Fair discrimination
#> AUC 0.7-0.8: Good discrimination
#> AUC 0.8-0.9: Excellent discrimination
#> AUC >0.9: Outstanding discrimination
#> Higher AUC indicates better staging system performance
RMST and Stage Migration Effect
Restricted Mean Survival Time (RMST) provides a clinically interpretable metric that does not depend on the proportional hazards assumption. The Stage Migration Effect (SME) formula quantifies the cumulative survival difference across stages.
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
calculateRMST = TRUE,
calculateSME = TRUE,
showStatisticalComparison = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> Statistical Comparison
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Metric Value 95% CI Interpretation
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging C-index 0.6258 [0.6096, 0.6420] Fair discrimination
#> New Staging C-index 0.6452 [0.6295, 0.6610] Fair discrimination
#> C-index Improvement +0.0194 [-0.0032, +0.0420] Small improvement
#> Relative Improvement +3.1% N/A Moderate
#> AIC Difference (Δ) 82.75 N/A Strong evidence for new model
#> BIC Difference (Δ) 82.75 N/A Very strong evidence
#> Clinical Significance No Threshold: 0.020 Below clinical threshold
#> Overall Recommendation 3/4 criteria met N/A Recommended for adoption
#> ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> LR Chi-Square Comparison (Key Staging Validation Metric)
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Staging System LR Chi-Square df p-value Goodness of Fit Model Quality
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> Original Staging System 233.4388474 3 < .0000001 Excellent fit Strong prognostic model
#> New Staging System 316.1839399 3 < .0000001 Excellent fit Strong prognostic model
#> LR Chi-Square Improvement 82.7450925 0 Substantial improvement New system better
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Note. LR Chi-Square measures model goodness-of-fit vs null model. Higher values indicate better prognostic
#> discrimination. This is a key metric for staging validation.
#>
#>
#> character(0)
#>
#> Stage Migration Effect Formula (SME) Results
#> ─────────────────────────────────────────────────────────────────
#> Timepoint SME Value Valid Comparisons Interpretation
#> ─────────────────────────────────────────────────────────────────
#> ─────────────────────────────────────────────────────────────────
#>
#>
#> Overall Stage Migration Effect Assessment
#> ─────────────────────────────────────────
#> Metric Value
#> ─────────────────────────────────────────
#> ─────────────────────────────────────────
#>
#>
#> character(0)
#>
#> RMST Analysis by Stage
#> ──────────────────────────────────────────────────────────────────────────────
#> Staging System Stage N Events RMST (months) Median Survival
#> ──────────────────────────────────────────────────────────────────────────────
#> ──────────────────────────────────────────────────────────────────────────────
#>
#>
#> RMST Discrimination Comparison
#> ──────────────────────────────────────────
#> System RMST Range Discrimination
#> ──────────────────────────────────────────
#> ──────────────────────────────────────────15. Visualizations
The module produces several publication-quality visualizations:
Migration Heatmap and Sankey Diagram
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "basic",
showMigrationHeatmap = TRUE,
showSankeyDiagram = TRUE,
showMigrationSurvivalComparison = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ───────────────────────────────────────────────
#> Statistic Value Percentage
#> ───────────────────────────────────────────────
#> Total Patients 2100.00000 100%
#> Unchanged Stage 1787.00000 85.1%
#> Migrated Stage 313.00000 14.9%
#> Upstaged 245.00000 11.7%
#> Downstaged 68.00000 3.2%
#> ───────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. Stage migration analysis completed
#> successfully for 2100 patients with 1155
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 496 66 29 0 591
#> Stage II 30 512 52 35 629
#> Stage III 0 31 400 63 494
#> Stage IV 0 0 7 379 386
#> ───────────────────────────────────────────────────────────────────────────
#>
#>
#> <div style="margin-bottom: 20px; padding: 15px; background-color:
#> #fff8e1; border-left: 4px solid #ffc107;">
#> <h4 style="margin-top: 0; color: #2c3e50;">Interpreting the Migration
#> Heatmap
#>
#> <p style="margin-bottom: 10px;">This heatmap visualizes patient
#> movement between staging systems:
#>
#> <ul style="margin-left: 20px;">
#> Y-axis (rows): Original staging system categories
#> X-axis (columns): New staging system categories
#> Color intensity: Darker blue = more patients
#> Numbers: Actual patient counts in each cell
#> Diagonal: Patients who remained in the same stage (no migration)
#>
#> <p style="margin-bottom: 5px;">Reading the heatmap:
#>
#> <ul style="margin-left: 20px;">
#> Cells above the diagonal = downstaging (patients moved to lower
#> stages)
#> Cells below the diagonal = upstaging (patients moved to higher stages)
#> Perfect agreement would show all patients on the diagonal
#> The pattern reveals systematic differences between staging systems
#> Error in `ggPalette()`:
#> ! Continuous value supplied to a discrete scale.
#> ℹ Example values: 496, 30, 0, 66, and 512.- Heatmap: Color-coded migration matrix; darker = more patients. The diagonal shows retention; off-diagonal shows migration flows.
- Sankey diagram: Flow visualization where band thickness represents patient count. Excellent for presentations.
- Migration survival comparison: KM curves showing how survival in each stage changes before and after reclassification.
16. Edge Cases and Small Samples
The module handles small samples gracefully, with appropriate warnings when sample size is insufficient for certain analyses.
stagemigration(
data = small_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "basic",
showMigrationOverview = TRUE,
showMigrationMatrix = TRUE,
showExplanations = TRUE
)
#>
#> ADVANCED TNM STAGE MIGRATION ANALYSIS
#>
#> Migration Overview
#> ──────────────────────────────────────────────
#> Statistic Value Percentage
#> ──────────────────────────────────────────────
#> Total Patients 50.000000 100%
#> Unchanged Stage 47.000000 94.0%
#> Migrated Stage 3.000000 6.0%
#> Upstaged 2.000000 4.0%
#> Downstaged 1.000000 2.0%
#> ──────────────────────────────────────────────
#> Note. Clinical preset 'routine_clinical'
#> selected. Presets are advisory; please
#> confirm displayed tables/plots and
#> advanced options match your scenario.
#> Note. NOTICE: 26 events detected.
#> Adequate for basic analysis but
#> bootstrap validation may be unstable.
#> For robust staging validation, 50+
#> events recommended.
#> Note. Stage migration analysis completed
#> successfully for 50 patients with 26
#> events. Review statistical comparisons
#> and clinical interpretation below.
#>
#>
#> Stage Migration Matrix
#> ───────────────────────────────────────────────────────────────────────────
#> Original Stage Stage I Stage II Stage III Stage IV Total
#> ───────────────────────────────────────────────────────────────────────────
#> Stage I 10 1 1 0 12
#> Stage II 1 13 0 0 14
#> Stage III 0 0 17 0 17
#> Stage IV 0 0 0 7 7
#> ───────────────────────────────────────────────────────────────────────────Data requirements:
- Minimum 30 patients (100+ recommended for standard analysis)
- At least 2 stage levels in both systems
- Event rate between 5% and 95%
- For bootstrap: 100+ patients recommended
- For NRI/IDI: adequate events at each time point
17. Reporting and Interpretation
Executive Summary and Copy-Ready Reports
stagemigration(
data = combined_data,
oldStage = "old_stage",
newStage = "new_stage",
survivalTime = "survival_time",
event = "event",
eventLevel = "1",
analysisType = "comprehensive",
calculateNRI = TRUE,
calculateIDI = TRUE,
performBootstrap = TRUE,
bootstrapReps = 100,
generateExecutiveSummary = TRUE,
generateCopyReadyReport = TRUE,
showClinicalInterpretation = TRUE,
showAbbreviationGlossary = TRUE
)
#> Error in `!is.null(results$clinical_interpretation) && grepl("recommend.*implementation",
#> results$clinical_interpretation$recommendation %||% "", ignore.case = TRUE)`:
#> ! 'length = 4' in coercion to 'logical(1)'The executive summary condenses all findings into a structured overview with key metrics and a staging system adoption recommendation. The copy-ready report generates plain-language paragraphs suitable for direct inclusion in manuscripts or clinical reports.
Complete Option Reference
Core Variables
| Option | Type | Default | Description |
|---|---|---|---|
oldStage |
Variable | - | Original TNM staging variable (factor) |
newStage |
Variable | - | Revised TNM staging variable (factor) |
survivalTime |
Variable | - | Follow-up time in months (numeric) |
event |
Variable | - | Event status indicator (numeric or factor) |
eventLevel |
Level | - | Level indicating event occurrence |
Analysis Control
| Option | Type | Default | Description |
|---|---|---|---|
analysisType |
List | comprehensive |
Scope: basic / standard / comprehensive / publication |
clinicalPreset |
List | routine_clinical |
Preset configuration: routine / research / publication / custom |
complexityMode |
List | quick |
UI complexity: quick / standard / comprehensive / custom |
confidenceLevel |
Number | 0.95 |
Confidence level for CIs and tests (0.80-0.99) |
cancerType |
List | general |
Cancer type for tailored thresholds |
Statistical Methods
| Option | Type | Default | Description |
|---|---|---|---|
calculateNRI |
Bool | FALSE |
Net Reclassification Improvement |
nriTimePoints |
String | "12, 24, 60" |
Comma-separated NRI time points (months) |
calculateIDI |
Bool | FALSE |
Integrated Discrimination Improvement |
performROCAnalysis |
Bool | FALSE |
Time-dependent ROC analysis |
rocTimePoints |
String | "12, 24, 36, 60" |
Comma-separated ROC time points |
performDCA |
Bool | FALSE |
Decision Curve Analysis |
performCalibration |
Bool | FALSE |
Calibration analysis |
performHomogeneityTests |
Bool | FALSE |
Within-stage homogeneity tests |
performTrendTests |
Bool | FALSE |
Monotonic trend across stages |
performLikelihoodTests |
Bool | FALSE |
Likelihood ratio tests |
calculatePseudoR2 |
Bool | FALSE |
Pseudo R-squared measures |
calculateSME |
Bool | FALSE |
Stage Migration Effect formula |
calculateRMST |
Bool | FALSE |
Restricted Mean Survival Time |
Validation
| Option | Type | Default | Description |
|---|---|---|---|
performBootstrap |
Bool | FALSE |
Bootstrap internal validation |
bootstrapReps |
Number | 1000 |
Bootstrap repetitions (100-2000) |
performCrossValidation |
Bool | FALSE |
k-fold cross-validation |
cvFolds |
Number | 5 |
Number of CV folds (3-10) |
useOptimismCorrection |
Bool | FALSE |
Apply optimism correction |
Clinical Thresholds
| Option | Type | Default | Description |
|---|---|---|---|
clinicalSignificanceThreshold |
Number | 0.02 |
Minimum C-index improvement considered clinically significant |
nriClinicalThreshold |
Number | 0.20 |
Minimum NRI for clinical meaningfulness |
Table Display
| Option | Type | Default | Description |
|---|---|---|---|
showMigrationOverview |
Bool | TRUE |
Overview table with key migration statistics |
showMigrationSummary |
Bool | FALSE |
Statistical summary with Chi-square / Fisher tests |
showStageDistribution |
Bool | FALSE |
Side-by-side stage distribution comparison |
showMigrationMatrix |
Bool | TRUE |
Detailed cross-tabulation matrix |
showStatisticalComparison |
Bool | FALSE |
C-index and other statistical metrics |
showConcordanceComparison |
Bool | FALSE |
Detailed concordance comparison |
showWillRogersAnalysis |
Bool | FALSE |
Will Rogers phenomenon analysis |
showClinicalInterpretation |
Bool | FALSE |
Clinical interpretation guide |
showStatisticalSummary |
Bool | FALSE |
Comprehensive statistical summary |
showMethodologyNotes |
Bool | FALSE |
Detailed methodology documentation |
showExplanations |
Bool | TRUE |
Explanatory text for results |
showAbbreviationGlossary |
Bool | FALSE |
Glossary of abbreviations |
includeEffectSizes |
Bool | FALSE |
Effect sizes for comparisons |
generateExecutiveSummary |
Bool | FALSE |
Key findings and recommendations |
Visualization
| Option | Type | Default | Description |
|---|---|---|---|
showMigrationHeatmap |
Bool | FALSE |
Color-coded migration heatmap |
showSankeyDiagram |
Bool | FALSE |
Patient flow diagram |
showROCComparison |
Bool | FALSE |
Time-dependent ROC curves |
showCalibrationPlots |
Bool | FALSE |
Calibration plots |
showDecisionCurves |
Bool | FALSE |
Decision curve plots |
showForestPlot |
Bool | FALSE |
Hazard ratio forest plot |
showWillRogersVisualization |
Bool | FALSE |
Will Rogers effect visualization |
showMigrationSurvivalComparison |
Bool | FALSE |
Before/after survival curves |
showSurvivalCurves |
Bool | FALSE |
Kaplan-Meier survival curves |
survivalPlotType |
List | separate |
Plot layout: separate / sidebyside / overlay |
showConfidenceIntervals |
Bool | FALSE |
CIs on survival curves |
showRiskTables |
Bool | FALSE |
At-risk tables below curves |
plotTimeRange |
String | "auto" |
Maximum time for plots (months or “auto”) |
Multifactorial Analysis
| Option | Type | Default | Description |
|---|---|---|---|
enableMultifactorialAnalysis |
Bool | FALSE |
Enable adjusted comparisons |
continuousCovariates |
Variables | NULL |
Continuous covariates (e.g., age) |
categoricalCovariates |
Variables | NULL |
Categorical covariates (e.g., sex) |
multifactorialComparisonType |
List | comprehensive |
adjusted_cindex / nested_models / stepwise / comprehensive |
baselineModel |
List | covariates_only |
Reference model for comparison |
performInteractionTests |
Bool | FALSE |
Test stage-covariate interactions |
stratifiedAnalysis |
Bool | FALSE |
Stratified subgroup analysis |
Competing Risks
| Option | Type | Default | Description |
|---|---|---|---|
performCompetingRisks |
Bool | FALSE |
Enable competing risks analysis |
competingEventVar |
Variable | NULL |
Competing event indicator variable |
performCompetingRisksAdvanced |
Bool | FALSE |
Advanced Fine-Gray analysis |
competingRisksMethod |
List | comprehensive |
finegray / causespecific / comprehensive |
Random Survival Forest
| Option | Type | Default | Description |
|---|---|---|---|
performRandomForestAnalysis |
Bool | FALSE |
Enable RSF analysis |
forestModelType |
List | rsf |
Forest model type |
forestNTrees |
Number | 500 |
Number of trees (100-5000) |
calculateVariableImportance |
Bool | FALSE |
Variable importance rankings |
forestDiscriminationMetrics |
Bool | FALSE |
RSF-based C-index |
User Experience
| Option | Type | Default | Description |
|---|---|---|---|
enableGuidedMode |
Bool | FALSE |
Step-by-step analysis guidance |
generateCopyReadyReport |
Bool | FALSE |
Manuscript-ready text output |
enableAccessibilityFeatures |
Bool | FALSE |
Color-blind safe palettes |
preferredLanguage |
List | en |
Output language: en / tr |
optimizeForLargeDatasets |
Bool | FALSE |
Memory-efficient processing for N > 10,000 |
Recommended Workflow
For a complete staging validation study, we recommend:
-
Start with the basics: Run
analysisType = "basic"to examine migration patterns, migration rates, and the cross-tabulation matrix - Assess discrimination: Enable C-index comparison and NRI/IDI to quantify improvement
- Check for Will Rogers: Always check for the Will Rogers phenomenon before claiming the new system is superior
- Validate internally: Use bootstrap validation with optimism correction to confirm findings are not over-optimistic
- Assess clinical utility: Decision curve analysis determines whether improved discrimination translates to better clinical decisions
-
Generate the report: Use
generateExecutiveSummaryandgenerateCopyReadyReportto produce manuscript-ready output
Or simply use clinicalPreset = "publication_ready" to
enable all of the above in one step.
References
Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157-172.
Feinstein AR, Sosin DM, Wells CK. The Will Rogers phenomenon. Stage migration and new diagnostic techniques as a source of misleading statistics for survival in cancer. N Engl J Med. 1985;312(25):1604-1608.
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-574.
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-387.
Amin MB, Edge SB, Greene FL, et al., eds. AJCC Cancer Staging Manual. 8th ed. Springer; 2017.
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496-509.
Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013;13:33.