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>.
|Depends:||R (≥ 2.10)|
|Imports:||nlme, riskRegression, dplyr, pec, methods, prodlim, stats, survival, mstate, ggplot2|
|Suggests:||testthat (≥ 3.0.0), knitr, rmarkdown, JM|
|Author:||Isobel Barrott [aut, cre], Jessica Barrett [aut], Ruth Keogh [ctb], Michael Sweeting [ctb], David Stevens [ctb]|
|Maintainer:||Isobel Barrott <isobel.barrott at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||Landmarking results|
Introduction to Landmark Models and the R package Landmarking
How to use the R package 'Landmarking'
|Windows binaries:||r-devel: Landmarking_1.0.0.zip, r-release: Landmarking_1.0.0.zip, r-oldrel: Landmarking_1.0.0.zip|
|macOS binaries:||r-release (arm64): Landmarking_1.0.0.tgz, r-oldrel (arm64): Landmarking_1.0.0.tgz, r-release (x86_64): Landmarking_1.0.0.tgz, r-oldrel (x86_64): Landmarking_1.0.0.tgz|
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