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spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

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  • Taylor, Benjamin M.
  • Rowlingson, Barry S.

Abstract

Survival methods are used for the statistical modelling of time-to-event data. Survival data are characterized 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. A particular feature of the new package is the ability to handle large datasets via the use of auxiliary frailties on a regular grid and the technique of circulant embedding for fast matrix computations. We demonstrate the new package on a real-life dataset.

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  • Taylor, Benjamin M. & Rowlingson, Barry S., 2017. "spatsurv: An R Package for Bayesian Inference with Spatial Survival Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i04).
    • Handle: RePEc:jss:jstsof:v:077:i04
    DOI: http://hdl.handle.net/10.18637/jss.v077.i04
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    References listed on IDEAS

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    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    2. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    3. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).
    4. Yi Li & Louise Ryan, 2002. "Modeling Spatial Survival Data Using Semiparametric Frailty Models," Biometrics, The International Biometric Society, vol. 58(2), pages 287-297, June.
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    Cited by:

    1. Minnie M. Joo & Brandon Bolte & Nguyen Huynh & Bumba Mukherjee, 2023. "Bayesian Spatial Split-Population Survival Model with Applications to Democratic Regime Failure and Civil War Recurrence," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    2. Anis Kausar Ghazali & Thomas Keegan & Benjamin M. Taylor, 2021. "Spatial Variation of Survival for Colorectal Cancer in Malaysia," IJERPH, MDPI, vol. 18(3), pages 1-12, January.

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