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Landmarking
View on CRAN: Click
here
Download and install Landmarking package within the R console
Install from CRAN:
install.packages("Landmarking")
Install from Github:
library("remotes")
install_github("cran/Landmarking")
Install by package version:
library("remotes")
install_version("Landmarking", "1.0.0")
Attach the package and use:
library("Landmarking")
Maintained by
Isobel Barrott
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-15
Latest Update: 2022-02-15
Description:
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
.
How to cite:
Isobel Barrott (2022). Landmarking: Analysis using Landmark Models. R package version 1.0.0, https://cran.r-project.org/web/packages/Landmarking
Previous versions and publish date:
No previous versions
Other packages that cited Landmarking R package
View Landmarking citation profile
Other R packages that Landmarking depends,
imports, suggests or enhances
Functions, R codes and Examples using
the Landmarking R package
Some associated functions: add_cv_number . data_repeat_outcomes . fit_LME_landmark . fit_LME_longitudinal . fit_LOCF_landmark . fit_LOCF_longitudinal . fit_survival_model . get_model_assessment . mixoutsamp . plot.landmark . predict.landmark . return_ids_with_LOCF .
Some associated R codes: add_cv_number.R . data.R . fit_LME_landmark.R . fit_LOCF_landmark.R . fit_survival_model.R . get_model_assessment.R . mixoutsamp.R . plot.landmark.R . predict.landmark.R . print.landmark.R . return_ids_with_LOCF.R . utils.R . Full Landmarking package functions and examples
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