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SemiCompRisks  

Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data
View on CRAN: Click here


Download and install SemiCompRisks package within the R console
Install from CRAN:
install.packages("SemiCompRisks")

Install from Github:
library("remotes")
install_github("cran/SemiCompRisks")

Install by package version:
library("remotes")
install_version("SemiCompRisks", "3.4")



Attach the package and use:
library("SemiCompRisks")
Maintained by
Kyu Ha Lee
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-12-21
Latest Update: 2021-02-03
Description:
Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows 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 and cluster-specific random effects distribution; 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 approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
How to cite:
Kyu Ha Lee (2013). SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data. R package version 3.4, https://cran.r-project.org/web/packages/SemiCompRisks. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2013-12-21 17:51), 2.0 (2015-02-08 00:51), 2.2 (2015-09-01 17:28), 2.3 (2015-12-04 21:43), 2.4 (2016-03-09 09:13), 2.5 (2016-10-27 11:09), 2.6 (2016-12-11 12:26), 2.7 (2017-06-28 19:34), 2.8 (2018-01-03 13:58), 3.0 (2018-03-30 20:44), 3.1 (2018-05-07 05:15), 3.2 (2019-01-20 23:30), 3.3 (2019-01-28 06:20)
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Maintainer: Qi Qin (view profile)

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