Net Reclassification Improvement (NRI)

Usage

netreclassification(
  data,
  outcome,
  baseline_risk,
  new_risk,
  time_var,
  followup_time = 5,
  nri_type = "categorical",
  risk_thresholds = "0.05, 0.10, 0.20",
  custom_categories = FALSE,
  category_labels = "Low, Intermediate, High, Very High",
  confidence_level = 0.95,
  bootstrap_samples = 1000,
  bootstrap_method = "bca",
  hypothesis_test = TRUE,
  decompose_nri = TRUE,
  direction_analysis = TRUE,
  show_transitions = TRUE,
  clinical_cutoffs = FALSE,
  treatment_threshold = 0.075,
  cost_effectiveness = FALSE,
  cross_validation = FALSE,
  cv_folds = 5,
  sensitivity_analysis = FALSE,
  show_summary = TRUE,
  show_reclassification = TRUE,
  show_performance = TRUE,
  plot_reclassification = TRUE,
  plot_risk_distribution = TRUE,
  plot_improvement = TRUE,
  plot_sensitivity = FALSE,
  competing_risks = FALSE,
  missing_handling = "complete",
  stratified_analysis = FALSE,
  subgroup_var,
  alpha_level = 0.05,
  random_seed = 123
)

Arguments

data

The data as a data frame.

outcome

Binary outcome variable (0/1) indicating event occurrence. For time-to-event data, this should be event status at the specified time point.

baseline_risk

Predicted risks or probabilities from the baseline (reference) model. Should be on probability scale (0-1) for risk predictions.

new_risk

Predicted risks or probabilities from the new (enhanced) model that includes additional predictors or biomarkers.

time_var

Time to event variable for survival data analysis. Required when using time-to-event outcomes.

followup_time

Time point for survival analysis (years). Events occurring after this time are considered censored for NRI calculation.

nri_type

Type of NRI analysis. Categorical uses predefined risk thresholds, numeric (risk-free) compares all risk changes, both provides comprehensive assessment.

risk_thresholds

Comma-separated risk thresholds for categorical NRI (e.g., 5\ for decision making.

custom_categories

Use custom category labels instead of automatic threshold-based naming. Allows for more clinically meaningful category descriptions.

category_labels

Comma-separated labels for risk categories when using custom categories. Should match the number of categories defined by thresholds.

confidence_level

Confidence level for NRI confidence intervals and hypothesis testing. 0.95 provides 95\

bootstrap_samplesNumber of bootstrap samples for confidence interval estimation. More samples provide more stable estimates but increase computation time.

bootstrap_methodBootstrap confidence interval method. BCa provides bias-corrected and accelerated intervals with better coverage properties.

hypothesis_testPerform hypothesis test for NRI = 0. Tests whether the new model provides significant reclassification improvement.

decompose_nriDecompose total NRI into contributions from events and non-events. Provides insight into which population drives the improvement.

direction_analysisAnalyze upward vs downward reclassification patterns separately. Shows whether improvement comes from better risk stratification.

show_transitionsDisplay detailed reclassification transition matrix showing movement between all risk categories.

clinical_cutoffsUse clinically established risk cutoffs (e.g., ACC/AHA guidelines) instead of data-driven thresholds for cardiovascular risk.

treatment_thresholdRisk threshold for treatment initiation. Used to calculate clinical decision metrics and treatment reclassification.

cost_effectivenessInclude cost-effectiveness considerations in reclassification analysis. Requires additional cost and utility parameters.

cross_validationPerform k-fold cross-validation to assess stability of NRI estimates and reduce optimism bias in model comparisons.

cv_foldsNumber of folds for cross-validation when enabled. 5-fold CV provides good balance of bias and variance.

sensitivity_analysisPerform sensitivity analysis by varying risk thresholds to assess robustness of NRI conclusions.

show_summaryDisplay comprehensive NRI summary including overall NRI, decomposed components, and statistical significance.

show_reclassificationDisplay detailed reclassification cross-tabulation showing movement patterns between baseline and new model categories.

show_performanceDisplay comparative performance metrics (C-statistic, calibration) for baseline and new models alongside NRI results.

plot_reclassificationCreate reclassification plot showing risk distribution changes and movement patterns between models.

plot_risk_distributionPlot risk distributions for baseline and new models separately for events and non-events with category boundaries.

plot_improvementVisualize NRI decomposition showing contributions from events and non-events with confidence intervals.

plot_sensitivityCreate plots showing how NRI varies with different risk thresholds to assess robustness of conclusions.

competing_risksAccount for competing risks in survival analysis. Modifies NRI calculation for settings with multiple event types.

missing_handlingMethod for handling missing data in risk predictions. Multiple imputation provides most robust results.

stratified_analysisPerform stratified NRI analysis by important subgroups (age, sex, baseline risk level) to assess consistency.

subgroup_varVariable defining subgroups for stratified analysis. Each level will receive separate NRI calculation.

alpha_levelType I error rate for hypothesis testing and confidence intervals. Standard value is 0.05 for 95\random_seedRandom seed for bootstrap sampling and cross-validation. Ensures reproducible results across analyses. A results object containing:

results$instructions					a html
results$todo					a html
results$summary					a table
results$reclassification					a table
results$decomposition					a table
results$performance					a table
results$transitions					a table
results$subgroupAnalysis					a table
results$sensitivityTable					a table
results$reclassificationPlot					an image
results$riskDistributionPlot					an image
results$improvementPlot					an image
results$sensitivityPlot					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:results$summary$asDFas.data.frame(results$summary) Net Reclassification Improvement (NRI) analysis for assessing the clinical utility of adding new biomarkers or risk factors to existing prediction models. NRI quantifies how well a new model reclassifies individuals into more appropriate risk categories compared to a baseline model. This implementation supports both categorical NRI (with predefined risk thresholds) and numeric NRI (risk-free approach). The analysis includes confidence intervals via bootstrap, statistical significance testing, and comprehensive visualization of reclassification patterns. Essential for biomarker validation studies, clinical decision making, and demonstrating incremental value of new predictors in medical research. Particularly valuable for risk prediction model development and validation in cardiovascular, oncology, and other clinical domains where risk stratification drives treatment decisions. result <- netreclassification( data = mydata, outcome = "event_indicator", baseline_risk = "baseline_predictions", new_risk = "new_model_predictions", risk_categories = c(0.05, 0.10, 0.20), time_point = 5, bootstrap_samples = 1000 )