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.

Arguments

Details

The package includes following functions:

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

Package:

SemiCompRisks

Type:

Package

Version:

2.4

Date:

2016-3-8

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. (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, in press.