| SemiCompRisks-package {SemiCompRisks} | R Documentation |
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 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: | 3.4 |
| Date: | 2021-2-2 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
Author(s)
Kyu Ha Lee, Catherine Lee, Danilo Alvares, and Sebastien Haneuse
Maintainer: Kyu Ha Lee <klee15239@gmail.com>
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.
Alvares, D., Haneuse, S., Lee, C., Lee, K. H. (2019),
SemiCompRisks: An R package for the analysis of independent and cluster-correlated semi-competing risks data, The R Journal, 11, 1, 376-400.