Package: SemiCompRisks 3.4

Kyu Ha Lee

SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data

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.

Authors:Kyu Ha Lee, Catherine Lee, Danilo Alvares, and Sebastien Haneuse

SemiCompRisks_3.4.tar.gz
SemiCompRisks_3.4.tar.gz(r-4.5-noble)SemiCompRisks_3.4.tar.gz(r-4.4-noble)
SemiCompRisks_3.4.tgz(r-4.3-emscripten)
SemiCompRisks.pdf |SemiCompRisks.html✨
SemiCompRisks/json (API)

# Install 'SemiCompRisks' in R:
install.packages('SemiCompRisks', repos = 'https://cloud.r-project.org')
Uses libs:
  • gsl– GNU Scientific Library (GSL)
Datasets:
  • BMT - Data on 137 Bone Marrow Transplant Patients
  • CIBMTR - Center for International Blood and Bone Marrow Transplant Research (CIBMTR) data
  • CIBMTR_Params - Estimates for model parameters from semi-competing risks analysis of the CIBMTR data using Weibull illness-death model.
  • scrData - A simulated clustered semi-competing risks data set
  • survData - A simulated clustered univariate survival data.

On CRAN:

Conda-Forge:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

gsl

2.41 score 26 scripts 501 downloads 1 mentions 16 exports 5 dependencies

Last updated 4 years agofrom:75c1efe6a7. Checks:1 ERROR, 1 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesFAILFeb 18 2025
R-4.5-linux-x86_64OKFeb 18 2025

Exports:BayesIDBayesID_AFTBayesID_HRegBayesSurvBayesSurv_AFTBayesSurv_HRegFreqIDFreqID_HRegFreqSurvFreqSurv_HReginitiate.startValuesinitiate.startValues_AFTinitiate.startValues_HRegPPDsimIDsimSurv

Dependencies:FormulalatticeMASSMatrixsurvival

This document presents a series of vignettes for the models available in SemiCompRisks package.

Rendered fromSemiCompRisks.ltxusingR.rsp::texon Feb 18 2025.

Last update: 2019-01-20
Started: 2016-10-27

Citation

To cite package ‘SemiCompRisks’ in publications use:

Lee KH, Lee C, Alvares D, Haneuse S (2021). SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data. R package version 3.4, https://CRAN.R-project.org/package=SemiCompRisks.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {SemiCompRisks: Hierarchical Models for Parametric and
      Semi-Parametric Analyses of Semi-Competing Risks Data},
    author = {Kyu Ha Lee and Catherine Lee and Danilo Alvares and
      Sebastien Haneuse},
    year = {2021},
    note = {R package version 3.4},
    url = {https://CRAN.R-project.org/package=SemiCompRisks},
  }

Readme and manuals

Help Manual

Help pageTopics
Algorithms for fitting parametric and semi-parametric models to semi-competing risks data / univariate survival data.SemiCompRisks-package SemiCompRisks
The function to implement Bayesian parametric and semi-parametric analyses for semi-competing risks data in the context of accelerated failure time (AFT) models.BayesID_AFT
The function to implement Bayesian parametric and semi-parametric analyses for semi-competing risks data in the context of hazard regression (HReg) models.BayesID_HReg
The function to implement Bayesian parametric and semi-parametric analyses for univariate survival data in the context of accelerated failure time (AFT) models.BayesSurv_AFT
The function to implement Bayesian parametric and semi-parametric regression analyses for univariate time-to-event data in the context of hazard regression (HReg) models.BayesSurv_HReg
Data on 137 Bone Marrow Transplant PatientsBMT
Center for International Blood and Bone Marrow Transplant Research (CIBMTR) dataCIBMTR
Estimates for model parameters from semi-competing risks analysis of the CIBMTR data using Weibull illness-death model.CIBMTR_Params
The function to fit parametric Weibull models for the frequentist anlaysis of semi-competing risks data.FreqID_HReg
The function to fit parametric Weibull models for the frequentist analysis of univariate survival data.FreqSurv_HReg
The function that initiates starting values for a single chain.initiate.startValues_AFT
The function that initiates starting values for a single chain.initiate.startValues_HReg
Methods for objects of classes, 'Bayes_HReg'/'Bayes_AFT'/'Freq_HReg'.coef.Bayes_AFT coef.Bayes_HReg coef.Freq_HReg plot.pred.Bayes_AFT plot.pred.Bayes_HReg plot.pred.Freq_HReg predict.Bayes_AFT predict.Bayes_HReg predict.Freq_HReg print.Bayes_AFT print.Bayes_HReg print.Freq_HReg print.summ.Bayes_AFT print.summ.Bayes_HReg print.summ.Freq_HReg summary.Bayes_AFT summary.Bayes_HReg summary.Freq_HReg vcov.Freq_HReg
Old functionsBayesID BayesSurv FreqID FreqSurv initiate.startValues
Function to predict the joint probability involving two event times in Bayesian illness-death modelsPPD
A simulated clustered semi-competing risks data setscrData
The function that simulates independent/cluster-correlated semi-competing risks data under semi-Markov Weibull/Weibull-MVN models.simID
The function that simulates independent/cluster-correlated right-censored survival data under Weibull/Weibull-Normal model.simSurv
A simulated clustered univariate survival data.survData