Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-038.zip
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Alvares, et al., "The R Journal: SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data", The R Journal, 2019
BibTeX citation
@article{RJ-2019-038,
author = {Alvares, Danilo and Haneuse, Sebastien and Lee, Catherine and Lee, Kyu Ha},
title = {The R Journal: SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-038},
doi = {10.32614/RJ-2019-038},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {376-400}
}