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Bayesian Spatial Survival Models

  • Chapter
Nonparametric Bayesian Inference in Biostatistics

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

Survival analysis has received a great deal of attention as a subfield of Bayesian nonparametrics over the last 50 years. In particular, the fitting of survival models that allow for sophisticated correlation structures has become common due to computational advances in the 1990s, in particular Markov chain Monte Carlo techniques. Very large, complex spatial datasets can now be analyzed accurately including the quantification of spatiotemporal trends and risk factors. This chapter reviews four nonparametric priors on baseline survival distributions in common use, followed by a catalogue of semiparametric and nonparametric models for survival data. Generalizations of these models allowing for spatial dependence are then discussed and broadly illustrated. Throughout, practical implementation through existing software is emphasized.

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Acknowledgements

This work was supported by federal grants 1R03CA165110 and 1R03CA176739-01A1.

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  1. Division of Statistics, Northern Illinois University, DeKalb, IL, 60115, USA

    Haiming Zhou

  2. Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA

    Timothy Hanson

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Correspondence to Timothy Hanson .

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  1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, USA

    Riten Mitra

  2. Department of Mathematics, University of Texas, Austin, Texas, USA

    Peter Müller

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Zhou, H., Hanson, T. (2015). Bayesian Spatial Survival Models. In: Mitra, R., Müller, P. (eds) Nonparametric Bayesian Inference in Biostatistics. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-19518-6_11

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