Time-to-event analysis using a wide array of longitudinal and survival sub-models.
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Time-to-event, or survival analysis, is used to analyse the time until an event of interest occurs. Common events include hospitalisation, equipment failure, or a prisoner reoffending. Whilst classic survival methods assume model covariates are static, it is often the case that longitudinal data related to the outcome of interest are collected. Two main forms of survival analysis incorporating time-dependent covariates exist, joint models and landmarking 1. This package focuses on the latter.
For a set of landmark times, a survival model is fitted up to specified horizon times. At landmark times, any time-dependent covariates must be summarised. Most commonly, the last observation carried forward (LOCF) approach is used. However, a more modern approach is to instead fit a linear mixed effects model which accounts for observations being measured with error 2. However, any method which summarises longitudinal observations can be used. Moreover, whilst landmarking methods typically reply on Cox proportional hazards models, nearly any survival model can also be used.
Whilst packages already exist which implement landmarking, these packages
implement specific longitudinal and survival models. The aim of landmaRk is
to support a wide array of longitudinal and survival sub-models whilst providing
a modular system allowing others to incorporate their own models.
We are planning to release the package on CRAN once the software is mature. For now, you can get a CRAN-like experience by installing from our r-universe
install.packages("landmaRk",
repos = c("https://vallejosgroup.r-universe.dev",
"https://cloud.r-project.org"))Alternatively, the package can be built from source using remotes
# install.packages("remotes")
remotes::install_github("vallejosgroup/landmaRk", build_vignettes = TRUE)We recommend starting with the landmaRk vignette, which provides an
overview of the package and how to use it. You can access the vignette in R by
calling
vignette("landmaRk")Alternatively, you can view the vignette online.
Footnotes
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Rizopoulos D, Molenberghs G, Lesaffre EMEH. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biometrical Journal. 2017;59(6):1261-1276. doi: 10.1002/bimj.201600238 ↩
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Paige E, Barrett J, Stevens D, et al. Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk. American Journal of Epidemiology. 2018;187(7):1530-1538. doi: 10.1093/aje/kwy018 ↩