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spatsurv: an R package for Bayesian inference with spatial survival models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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spatsurv: an R package for Bayesian inference with spatial survival models. / Taylor, Benjamin; Rowlingson, Barry.
In: Journal of Statistical Software, Vol. 77, No. 4, 31.03.2017, p. 1-32.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Taylor, B & Rowlingson, B 2017, 'spatsurv: an R package for Bayesian inference with spatial survival models', Journal of Statistical Software, vol. 77, no. 4, pp. 1-32. <https://www.jstatsoft.org/article/view/v077i04>

APA

Taylor, B., & Rowlingson, B. (2017). spatsurv: an R package for Bayesian inference with spatial survival models. Journal of Statistical Software, 77(4), 1-32. https://www.jstatsoft.org/article/view/v077i04

Vancouver

Taylor B, Rowlingson B. spatsurv: an R package for Bayesian inference with spatial survival models. Journal of Statistical Software. 2017 Mar 31;77(4):1-32.

Author

Taylor, Benjamin ; Rowlingson, Barry. / spatsurv : an R package for Bayesian inference with spatial survival models. In: Journal of Statistical Software. 2017 ; Vol. 77, No. 4. pp. 1-32.

Bibtex

@article{bd8b7b9073124a8bb4349f7c8b14754c,
title = "spatsurv: an R package for Bayesian inference with spatial survival models",
abstract = "Survival methods are used for the statistical modelling of time-to-event data, with applications in many scientific fields. Survival data are characterised by a set of complete records, in which the time of the event is known; and a set of censored records, in which the event was known to have occurred in an interval. When survival data are spatially referenced, the spatial variation in survival times may be of scientific interest. In this article, we introduce a new R package, spatsurv, for inference with spatially referenced survival data. The specific type of model fitted by this package is a parametric proportional hazards model in which the spatially correlated frailties are modelled by a log-Gaussian stochastic process. The package is extensible in that it allows the user to easily create new models for the baseline hazard function and spatial covariance function. The package implements an advanced adaptive Markov chain Monte Carlo algorithm to deliver Bayesian inference with minimal input from the user.",
keywords = "Spatial Survival, Correlated Frailties, Parametric Proportional Hazards, LogGaussian Frailties",
author = "Benjamin Taylor and Barry Rowlingson",
year = "2017",
month = mar,
day = "31",
language = "English",
volume = "77",
pages = "1--32",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "4",

}

RIS

TY - JOUR

T1 - spatsurv

T2 - an R package for Bayesian inference with spatial survival models

AU - Taylor, Benjamin

AU - Rowlingson, Barry

PY - 2017/3/31

Y1 - 2017/3/31

N2 - Survival methods are used for the statistical modelling of time-to-event data, with applications in many scientific fields. Survival data are characterised by a set of complete records, in which the time of the event is known; and a set of censored records, in which the event was known to have occurred in an interval. When survival data are spatially referenced, the spatial variation in survival times may be of scientific interest. In this article, we introduce a new R package, spatsurv, for inference with spatially referenced survival data. The specific type of model fitted by this package is a parametric proportional hazards model in which the spatially correlated frailties are modelled by a log-Gaussian stochastic process. The package is extensible in that it allows the user to easily create new models for the baseline hazard function and spatial covariance function. The package implements an advanced adaptive Markov chain Monte Carlo algorithm to deliver Bayesian inference with minimal input from the user.

AB - Survival methods are used for the statistical modelling of time-to-event data, with applications in many scientific fields. Survival data are characterised by a set of complete records, in which the time of the event is known; and a set of censored records, in which the event was known to have occurred in an interval. When survival data are spatially referenced, the spatial variation in survival times may be of scientific interest. In this article, we introduce a new R package, spatsurv, for inference with spatially referenced survival data. The specific type of model fitted by this package is a parametric proportional hazards model in which the spatially correlated frailties are modelled by a log-Gaussian stochastic process. The package is extensible in that it allows the user to easily create new models for the baseline hazard function and spatial covariance function. The package implements an advanced adaptive Markov chain Monte Carlo algorithm to deliver Bayesian inference with minimal input from the user.

KW - Spatial Survival

KW - Correlated Frailties

KW - Parametric Proportional Hazards

KW - LogGaussian Frailties

M3 - Journal article

VL - 77

SP - 1

EP - 32

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 4

ER -