Bayesian Modeling and Analysis of Spatially Correlated Survival Data

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Description

This package provides several Bayesian survival models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized accelerated failure time frailty models, and standard semiparametric frailty models within the context of proportional hazards, proportional odds and accelerated failure time.

Details

Package: spBayesSurv
Type: Package
Version: 1.0.5
License: GPL (>= 2)

This package provides several Bayesian survival models for spatial/non-spatial survival data, including marginal Bayesian Nonparametric models, where spCopulaDDP is for point-referenced data and anovaDDP is for non-spatial data; marginal Bayesian proportional hazards models, where spCopulaCox is for point-referenced data and indeptCoxph is for non-spatial data; generalized accelerated failure time frailty models via frailtyGAFT for both spatial and non-spatial survival data; and standard semiparametric frailty models via survregbayes and survregbayes2 within the context of proportional hazards, proportional odds and accelerated failure time.

Author(s)

Haiming Zhou <zhouh@niu.edu> and Tim Hanson <hansont@stat.sc.edu>

References

De Iorio, M., Johnson, W. O., Mueller, P., and Rosner, G. L. (2009). Bayesian nonparametric nonproportional hazards survival modeling. Biometrics, 65(3): 762-771.

Zhou, H., Hanson, T., and Knapp, R. (2015). Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations. Biometrics, 71(4): 1101-1110.

Zhou, H. and Hanson, T. (2015). Bayesian spatial survival models. In Nonparametric Bayesian Inference in Biostatistics (pp. 215-246). Springer International Publishing.

Zhou, H., Hanson, T., and Zhang, J. (2016). Generalized accelerated failure time spatial frailty model for arbitrarily censored data. Lifetime Data Analysis, in press.

Zhou, H. and Hanson, T. (2016). Bayesian semiparametric models for spatially correlated arbitrarily censored data. In preparation.