Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data
The function to implement Bayesian parametric and semi-parametric anal...
The function to implement Bayesian parametric and semi-parametric anal...
The function to implement Bayesian parametric and semi-parametric anal...
The function to implement Bayesian parametric and semi-parametric regr...
The function to fit parametric Weibull models for the frequentist anla...
The function to fit parametric Weibull models for the frequentist anal...
The function that initiates starting values for a single chain.
The function that initiates starting values for a single chain.
Methods for objects of classes, Bayes_HReg/Bayes_AFT/Freq_HReg.
Old functions
Function to predict the joint probability involving two event times in...
Algorithms for fitting parametric and semi-parametric models to semi-c...
The function that simulates independent/cluster-correlated semi-compet...
The function that simulates independent/cluster-correlated right-censo...
Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.