Landmark analysis for survival data with time-varying predictors. Provides unbiased estimates by analyzing survival from fixed landmark times.
Usage
landmarkanalysis(
data,
time,
status,
predictors,
landmark_times = "6, 12, 24",
prediction_window = 12,
min_events = 10,
include_baseline = TRUE,
dynamic_prediction = TRUE,
calibration_plot = TRUE,
discrimination_plot = TRUE,
supermodel = FALSE,
bootstrap_validation = FALSE,
n_bootstrap = 200
)Arguments
- data
The data as a data frame.
- time
Follow-up time variable
- status
Event status variable
- predictors
Predictor variables for landmark analysis
- landmark_times
Vector of landmark time points
- prediction_window
Length of prediction window from each landmark
- min_events
Minimum events threshold for landmark analysis
- include_baseline
Whether to include baseline Cox model
- dynamic_prediction
Calculate time-dependent risk predictions
- calibration_plot
Generate calibration assessment plots
- discrimination_plot
Generate discrimination assessment plots
- supermodel
Combine landmark models using super model
- bootstrap_validation
Use bootstrap for model validation
- n_bootstrap
Number of bootstrap replications
Value
A results object containing:
results$todo | a html | ||||
results$summary | a html | ||||
results$baselineModel | a table | ||||
results$landmarkResults | a table | ||||
results$modelComparison | a table | ||||
results$dynamicPredictionPlot | an image | ||||
results$calibrationPlot | an image | ||||
results$discriminationPlot | an image | ||||
results$superModelResults | a table | ||||
results$validationResults | a html | ||||
results$clinicalGuidance | a html | ||||
results$technicalNotes | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$baselineModel$asDF
as.data.frame(results$baselineModel)