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Geostatistical survival models for environmental risk assessment with large retrospective cohorts

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  • Huan Jiang
  • Patrick E. Brown
  • Håvard Rue
  • Silvia Shimakura

Abstract

type="main" xml:id="rssa12041-abs-0001"> Motivated by the problem of cancer risk assessment near a nuclear power generating station, the paper describes a methodology for fitting a spatially correlated survival model to large retrospective cohort data sets. Retrospective cohorts, which can be assembled inexpensively from population-based health databases, can partially account for lags between exposures and outcome of chronic diseases such as cancer. These data sets overcome one of the principal limitations of cross-sectional spatial analyses, though performing statistical inference requires accommodating censored and truncated event times as well as spatial dependence. The use of spatial survival models for large retrospective cohorts is described, and Bayesian inference using Markov random-field approximations and integrated nested Laplace approximations is presented. The method is applied to data from individuals living near Pickering Nuclear Generating Station in Canada, showing that the effect of ambient radiation on cancer is not statistically significant.

Suggested Citation

  • Huan Jiang & Patrick E. Brown & Håvard Rue & Silvia Shimakura, 2014. "Geostatistical survival models for environmental risk assessment with large retrospective cohorts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(3), pages 679-695, June.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:3:p:679-695
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-3
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    Cited by:

    1. Brown, Patrick E., 2015. "Model-Based Geostatistics the Easy Way," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i12).
    2. Gressani, Oswaldo & Lambert, Philippe, 2016. "Fast Bayesian inference in semi-parametric P-spline cure survival models using Laplace approximations," LIDAM Discussion Papers ISBA 2016041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Gressani, Oswaldo & Lambert, Philippe, 2018. "Fast Bayesian inference using Laplace approximations in a flexible promotion time cure model based on P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 151-167.

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