SemiCompRisks-package: Algorithms for fitting parametric and semi-parametric models to semi-competing risks data / univariate survival data.
Description
The package provides functions to perform the analysis of semi-competing risks or univariate survival data. 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:
ll{
BayesID Bayesian analysis of semi-competing risks data
BayesSurv Bayesian analysis of univariate survival data
FreqID Frequentist analysis of semi-competing risks data
FreqSurv Frequentist analysis of univariate survival data
initiate.startValues Initiating starting values for Bayesian estimations
simID Simulating semi-competing risks data under Weibull/Weibull-MVN model
simSurv Simulating survival data under Weibull/Weibull-Normal model
}
ll{
Package: SemiCompRisks
Type: Package
Version: 2.2
Date: 2015-8-31
License: GPL (>= 2)
LazyLoad: yes
}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.,
Hierarchical models for cluster-correlated semicompeting risks data with application to quality of end-of-life care for pancreatic cancer, arXiv:1502.00526; submitted.