SemiCompRisks-package: Algorithms for fitting parametric and semi-parametric models...

Description Details Author(s) References

Description

The package provides functions to perform the analysis of semi-competing risks or univariate survival data with either hazard regression (HReg) models or accelerated failure time (AFT) models. The framework is flexible in the sense that:
1) it can handle cluster-correlated or independent data;
2) the option to choose between parametric (Weibull) and semi-parametric (mixture of piecewise exponential) specification for baseline hazard function(s) is available;
3) for clustered data, the option to choose between parametric (multivariate Normal for semicompeting risks data, Normal for univariate survival data) and semi-parametric (Dirichlet process mixture) specification for random effects distribution is available;
4) for semi-competing risks data, the option to choose between Makov and semi-Makov model is available.

Details

The package includes following functions:

BayesID_HReg Bayesian analysis of semi-competing risks data using HReg models
BayesID_AFT Bayesian analysis of semi-competing risks data using AFT models
BayesSurv_HReg Bayesian analysis of univariate survival data using HReg models
BayesSurv_AFT Bayesian analysis of univariate survival data using AFT models
FreqID_HReg Frequentist analysis of semi-competing risks data using HReg models
FreqSurv_HReg Frequentist analysis of univariate survival data using HReg models
initiate.startValues_HReg Initiating starting values for Bayesian estimations with HReg models
initiate.startValues_AFT Initiating starting values for Bayesian estimations with AFT models
simID Simulating semi-competing risks data under Weibull/Weibull-MVN model
simSurv Simulating survival data under Weibull/Weibull-Normal model
Package: SemiCompRisks
Type: Package
Version: 2.8
Date: 2018-1-3
License: GPL (>= 2)
LazyLoad: yes

Author(s)

Kyu Ha Lee, Catherine Lee, Danilo Alvares, and Sebastien Haneuse
Maintainer: Kyu Ha Lee <[email protected]>

References

Lee, K. H., Haneuse, S., Schrag, D., and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis, Journal of the Royal Statistical Society: Series C, 64, 2, 253-273.

Lee, K. H., Dominici, F., Schrag, D., and Haneuse, S. (2016), Hierarchical models for semicompeting risks data with application to quality of end-of-life care for pancreatic cancer, Journal of the American Statistical Association, 111, 515, 1075-1095.

Lee, K. H., Rondeau, V., and Haneuse, S. (2017), Accelerated failure time models for semicompeting risks data in the presence of complex censoring, Biometrics, 73, 4, 1401-1412.


SemiCompRisks documentation built on Jan. 3, 2018, 10:50 p.m.