spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Dublin Core
Title
spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Subject
Bayesian nonparametric, survival analysis, spatial dependence, semiparametric
models, parametric models
models, parametric models
Description
Spatial survival analysis has received a great deal of attention over the last 20 years
due to the important role that geographical information can play in predicting survival.
This paper provides an introduction to a set of programs for implementing some Bayesian
spatial survival models in R using the package spBayesSurv. The function survregbayes
includes the three most commonly-used semiparametric models: proportional hazards,
proportional odds, and accelerated failure time. All manner of censored survival times
are simultaneously accommodated including uncensored, interval censored, current-status,
left and right censored, and mixtures of these. Left-truncated data are also accommodated. Time-dependent covariates are allowed under the piecewise constant assumption.
Both georeferenced and areally observed spatial locations are handled via frailties. Model
fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via
the log pseudo marginal likelihood, the deviance information criterion and the WatanabeAkaike information criterion. The accelerated failure time frailty model with a covariatedependent baseline is included in the function frailtyGAFT. In addition, the package
also provides two marginal survival models: proportional hazards and linear dependent
Dirichlet process mixtures, where the spatial dependence is modeled via spatial copulas.
Note that the package can also handle non-spatial data using non-spatial versions of the
aforementioned models.
due to the important role that geographical information can play in predicting survival.
This paper provides an introduction to a set of programs for implementing some Bayesian
spatial survival models in R using the package spBayesSurv. The function survregbayes
includes the three most commonly-used semiparametric models: proportional hazards,
proportional odds, and accelerated failure time. All manner of censored survival times
are simultaneously accommodated including uncensored, interval censored, current-status,
left and right censored, and mixtures of these. Left-truncated data are also accommodated. Time-dependent covariates are allowed under the piecewise constant assumption.
Both georeferenced and areally observed spatial locations are handled via frailties. Model
fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via
the log pseudo marginal likelihood, the deviance information criterion and the WatanabeAkaike information criterion. The accelerated failure time frailty model with a covariatedependent baseline is included in the function frailtyGAFT. In addition, the package
also provides two marginal survival models: proportional hazards and linear dependent
Dirichlet process mixtures, where the spatial dependence is modeled via spatial copulas.
Note that the package can also handle non-spatial data using non-spatial versions of the
aforementioned models.
Creator
Haiming Zhou
Source
https://www.jstatsoft.org/article/view/v092i09
Publisher
Northern Illinois University
Date
February 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Haiming Zhou, “spBayesSurv: Fitting Bayesian Spatial Survival Models Using R,” Repository Horizon University Indonesia, accessed May 10, 2025, https://repository.horizon.ac.id/items/show/8113.