Survival Model Validation — survivalvalidation • ClinicoPath

Comprehensive validation and performance assessment for survival models. Includes prediction error curves, time-dependent ROC analysis, calibration plots, and decision curve analysis for clinical decision making.

Usage

survivalvalidation(
  data,
  time,
  status,
  predicted_risk,
  model_formula = "",
  external_data,
  validation_method = "cv",
  cv_folds = 10,
  bootstrap_samples = 500,
  concordance_index = TRUE,
  time_dependent_auc = TRUE,
  prediction_error = TRUE,
  integrated_brier = TRUE,
  calibration_plot = TRUE,
  decision_curve = TRUE,
  time_points = "1,2,3,5",
  max_time = 0,
  plot_roc_curves = TRUE,
  plot_calibration = TRUE,
  plot_decision_curve = TRUE,
  plot_prediction_error = TRUE,
  confidence_level = 0.95,
  smoothing = TRUE,
  risk_groups = 4,
  competing_risks = FALSE,
  cause_specific,
  model_comparison = FALSE,
  model_names = "",
  net_benefit_thresholds = "0.01,0.05,0.1,0.2,0.3"
)

Arguments

data: The data as a data frame.
time: Time to event or censoring
status: Event indicator variable
predicted_risk: Model predictions to validate
model_formula: Variables for Cox model if predictions not provided
external_data: Optional external dataset for validation
validation_method: Validation approach
cv_folds: CV fold specification
bootstrap_samples: Bootstrap iterations
concordance_index: Compute C-index
time_dependent_auc: Compute time-dependent AUC
prediction_error: Compute prediction error curves
integrated_brier: Compute IBS
calibration_plot: Create calibration assessment
decision_curve: DCA for clinical decision making
time_points: Specific times for assessment
max_time: Upper time limit
plot_roc_curves: Generate ROC plots
plot_calibration: Generate calibration plots
plot_decision_curve: Generate DCA plots
plot_prediction_error: Generate PEC plots
confidence_level: CI level
smoothing: Smooth curve estimation
risk_groups: Risk group stratification
competing_risks: Competing risks consideration
cause_specific: Cause-specific event variable
model_comparison: Multi-model comparison
model_names: Model labels for comparison
net_benefit_thresholds: DCA threshold range

Value

A results object containing:

`results$todo`					a html
`results$summary`					a html
`results$performanceTable`					a table
`results$concordanceTable`					a table
`results$aucTable`					a table
`results$brierTable`					a table
`results$calibrationTable`					a table
`results$decisionTable`					a table
`results$rocPlot`					an image
`results$calibrationPlot`					an image
`results$decisionPlot`					an image
`results$predErrorPlot`					an image
`results$calibrationMetrics`					a table
`results$validationSummary`					a html
`results$modelComparison`					a table
`results$externalValidation`					a html
`results$competingRisksMetrics`					a table
`results$interpretation`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$performanceTable$asDF

as.data.frame(results$performanceTable)

Examples

# \donttest{
# Example: Validate Cox regression model
survivalvalidation(
    data = cancer_data,
    time = followup_time,
    status = death_status,
    predicted_risk = risk_score,
    validation_method = "cv",
    time_points = c(1, 3, 5)
)
# }