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.
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 |
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.