spBayesSurv: Fitting Bayesian Spatial Survival Models Using R

Author ORCID Identifier

Haiming Zhou:https://orcid.org/0000-0002-2777-5354

Publication Title

Journal of Statistical Software

ISSN

15487660

E-ISSN

43888

Document Type

Article

Abstract

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 Watanabe-Akaike information criterion. The accelerated failure time frailty model with a covariate-dependent 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.

Publication Date

1-1-2020

DOI

10.18637/jss.v092.i09

Keywords

Bayesian nonparametric, Parametric models, Semiparametric models, Spatial dependence, Survival analysis

Department

Department of Statistics and Actuarial Science

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