Package bayesSurv
This package implements several random effects accelerated failure time models (AFT) for right-, left-, interval-, and
doubly-interval censored data fitted using Markov chain Monte Carlo (MCMC) methodology.
Models can be divided into four methodological groups which are described below. Additional information can also be found
at personal webpage of Arnošt Komárek.
The following models are used to model the distributional parts in the AFT model
- Normal distribution
- Classical normal mixture which is a normal mixture with estimated number of components, estimated means and variances
of these components and estimated weights of these components
- Penalized Gaussian mixture (PGM) also called as G-spline. This is a normal mixture with a higher number of
homoscedastic components, specified over a fixed grid of equidistant knots - means. Only the weight of the mixture are estimated
and roughness penalty is imposed on weights in the estimation procedure.
1. AFT with a classical normal mixture as an error distribution and normal random effects
Methodology has been published in
Main functions from the package related to this methodology
Secondary functions from the package related to this methodology
Examples
2. AFT with a penalized Gaussian mixture as an error distribution and normal random effects
Methodology has been published in
Main functions from the package related to this methodology
Secondary functions from the package related to this methodology
Examples
- ex-eortc.pdf - example 3 using the data set from EORTC trial 10854 (unpublic)
3. AFT with a penalized Gaussian mixture as an error distribution and random effects whose distribution is a penalized Gaussian mixture
Methodology has been published in
Main functions from the package related to this methodology
Secondary functions from the package related to this methodology
Examples
4. AFT model for paired data with a bivariate penalized Gaussian mixture as an error distribution
Methodology has been published in
Main functions from the package related to this methodology
Secondary functions from the package related to this methodology
Examples