Analysis using Landmark Models
Assign a k-fold cross-validation number
Fit a landmarking model using a linear mixed effects (LME) model for t...
Fit a landmarking model using a linear mixed effects (LME) model for t...
Fit a landmark model using a last observation carried forward (LOCF) m...
Find the last observation carried forward (LOCF) values for covariates...
Fit a survival sub-model
Compute C-index and Brier score
Calculate point estimates from a linear mixed effects (LME) model for ...
Create a calibration plot
Predict the risk of an event for a new individual using the landmark m...
Select individuals in a dataset with a last observation carried forwar...
The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.
Useful links