The R Journal: accepted article

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BayesSPsurv: An R Package to Estimate Bayesian (Spatial) Split-Population Survival Models PDF download
Brandon Bolte, Nicolás Schmidt, Sergio Béjar, Nguyen Huynh and Bumba Mukherjee

Abstract Survival data often include a fraction of units that are susceptible to an event of interest as well as a fraction of “immune” units. In many applications, spatial clustering in unobserved risk factors across nearby units can also affect their survival rates and odds of becoming immune. To address these methodological challenges, this article introduces our BayesSPsurv R-package, which fits parametric Bayesian Spatial split-population survival (cure) models that can account for spatial autocorrelation in both subpopulations of the user’s time-to-event data. Spatial autocorrelation is modeled with spatially weighted frailties, which are estimated using a conditionally autoregressive prior. The user can also fit parametric cure models with or without non-spatial i.i.d. frailties, and each model can incorporate time-varying covariates. BayesSPsurv also includes various functions to conduct pre-estimation spatial autocorrelation tests, visualize results, and assess model performance, all of which are illustrated using data on post-civil war peace survival.

Received: 2021-02-01; online 2021-07-15
CRAN packages: BayesSPsurv, survival, dynsurv, smcure, nltm, flexsurvcure, spduration, BayesX, R2BayesX, spBayesSurv, spatsurv, Rcpp, coda, doParallel, doRNG, countrycode, rworldmap
CRAN Task Views implied by cited CRAN packages: Survival, Bayesian, HighPerformanceComputing, Spatial, ClinicalTrials, Econometrics, gR, NumericalMathematics, OfficialStatistics, SocialSciences


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-068,
  author = {Brandon Bolte and Nicolás Schmidt and Sergio Béjar and
          Nguyen Huynh and Bumba Mukherjee},
  title = {{BayesSPsurv: An R Package to Estimate Bayesian (Spatial)
          Split-Population Survival Models}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-068},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-068/index.html}
}