R: Analysis using Landmark Models

# Analysis using Landmark Models

## Documentation for package ‘Landmarking’ version 1.0.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

add_cv_number | Assign a k-fold cross-validation number
data_repeat_outcomes | Simulated repeat measurement and time-to-event data
find_LME_risk_set | Find the risk set for a landmark model (LME)
find_LOCF_risk_set | Find the risk set for a landmark model (LOCF)
fit_LME_landmark | Fit a landmarking model using a linear mixed effects (LME) model for the longitudinal submodel
fit_LME_longitudinal | Fit a landmarking model using a linear mixed effects (LME) model for the longitudinal data
fit_LOCF_landmark | Fit a landmark model using a last observation carried forward (LOCF) method for the longitudinal data
fit_LOCF_longitudinal | Find the last observation carried forward (LOCF) values for covariates in a dataset
fit_survival_model | Fit a survival sub-model as part of a landmarking analysis
get_model_assessment | Compute C-index and Brier score
mixoutsamp | Calculate point estimates from a linear mixed effects (LME) model for new data
plot.landmark | Create a calibration plot
predict.landmark | Predict the risk of an event for a new individual using the landmark model
return_ids_with_LOCF | Select individuals in a dataset with a last observation carried forward (LOCF) at a landmark time