Analysis of time-dependent covariates and time-varying ROC curves for survival data. Handles covariates that change over time, landmark analysis, and dynamic prediction accuracy assessment through time-dependent AUC and optimal cutpoint selection.
Usage
timedependent(
data,
id,
start_time,
stop_time,
event,
time_dependent_vars,
baseline_vars,
perform_landmark = TRUE,
landmark_times = "6,12,24",
prediction_window = 12,
time_dependent_roc = TRUE,
roc_times = "1,2,3,5",
roc_method = "incident_dynamic",
optimal_cutpoint = TRUE,
cutpoint_method = "youden",
model_type = "extended_cox",
time_transform = "none",
test_proportional_hazards = TRUE,
schoenfeld_transform = "km",
internal_validation = TRUE,
cv_folds = 5,
bootstrap_validation = FALSE,
n_bootstrap = 100,
compare_models = FALSE,
comparison_metric = "iauc",
plot_time_varying_effects = TRUE,
plot_roc_curves = TRUE,
plot_auc_trajectory = TRUE,
plot_cutpoint_stability = TRUE,
plot_landmark_predictions = TRUE,
plot_schoenfeld_residuals = TRUE,
confidence_level = 0.95,
decimals = 3,
export_predictions = FALSE,
export_roc_data = FALSE
)Arguments
- data
The data as a data frame (can be in counting process format).
- id
Patient ID variable
- start_time
Beginning of time interval (tstart)
- stop_time
End of time interval (tstop)
- event
Event status variable
- time_dependent_vars
Time-varying predictors
- baseline_vars
Time-fixed predictors
- perform_landmark
Enable landmark analysis
- landmark_times
Landmark time points
- prediction_window
Prediction horizon from landmark
- time_dependent_roc
Enable TD-ROC analysis
- roc_times
ROC assessment times
- roc_method
TD-ROC approach
- optimal_cutpoint
Optimal threshold selection
- cutpoint_method
Cutpoint optimization approach
- model_type
Model specification
- time_transform
Time function transformation
- test_proportional_hazards
PH assumption testing
- schoenfeld_transform
Residual test transformation
- internal_validation
Cross-validation assessment
- cv_folds
CV fold specification
- bootstrap_validation
Bootstrap assessment
- n_bootstrap
Bootstrap iterations
- compare_models
Model comparison
- comparison_metric
Comparison measure
- plot_time_varying_effects
Time-varying coefficient plots
- plot_roc_curves
TD-ROC visualization
- plot_auc_trajectory
AUC evolution plot
- plot_cutpoint_stability
Cutpoint trajectory plot
- plot_landmark_predictions
Landmark prediction plots
- plot_schoenfeld_residuals
Residual diagnostic plots
- confidence_level
CI level
- decimals
Output precision
- export_predictions
Save predictions
- export_roc_data
Save ROC results
Value
A results object containing:
results$todo | a html | ||||
results$model_summary | a html | ||||
results$cox_results | a table | ||||
results$time_varying_effects | a table | ||||
results$landmark_results | a table | ||||
results$roc_results | a table | ||||
results$model_comparison | a table | ||||
results$validation_results | a table | ||||
results$time_varying_plot | an image | ||||
results$roc_curves_plot | an image | ||||
results$auc_trajectory_plot | an image | ||||
results$cutpoint_stability_plot | an image | ||||
results$landmark_predictions_plot | an image | ||||
results$schoenfeld_plot | an image | ||||
results$interpretation | a html | ||||
results$recommendations | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$cox_results$asDF
as.data.frame(results$cox_results)
Examples
# \donttest{
# Example: Time-varying biomarker effects
timedependent(
data = biomarker_data,
id = patient_id,
start_time = tstart,
stop_time = tstop,
event = status,
time_dependent_vars = c("biomarker_level", "treatment_status"),
baseline_vars = c("age", "sex"),
landmark_times = c(6, 12, 24),
roc_times = c(1, 2, 3, 5)
)
# }