Analysis using Landmark Models

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Documentation for package ‘Landmarking’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_cv_number	Assign a k-fold cross-validation number
data_repeat_outcomes	Simulated repeat measurement and time-to-event data
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 submodel
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
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