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:
||Bayesian analysis of semi-competing risks data using HReg models|
||Bayesian analysis of semi-competing risks data using AFT models|
||Bayesian analysis of univariate survival data using HReg models|
||Bayesian analysis of univariate survival data using AFT models|
||Frequentist analysis of semi-competing risks data using HReg models|
||Frequentist analysis of univariate survival data using HReg models|
||Initiating starting values for Bayesian estimations with HReg models|
||Initiating starting values for Bayesian estimations with AFT models|
||Simulating semi-competing risks data under Weibull/Weibull-MVN model|
||Simulating survival data under Weibull/Weibull-Normal model|
|License:||GPL (>= 2)|
Kyu Ha Lee, Catherine Lee, Danilo Alvares, and Sebastien Haneuse
Maintainer: Kyu Ha Lee <[email protected]>
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. (2018+), SemiCompRisks: an R package for independent and cluster-correlated analyses of semi-competing risks data, submitted, arXiv:1801.03567.
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