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Geostatistical survival models for environmental risk assessment with large retrospective cohorts
Author(s) -
Jiang Huan,
Brown Patrick E.,
Rue Håvard,
Shimakura Silvia
Publication year - 2014
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12041
Subject(s) - laplace's method , retrospective cohort study , statistics , markov random field , computer science , bayesian probability , population , inference , bayesian inference , cancer registry , econometrics , data mining , medicine , mathematics , artificial intelligence , environmental health , segmentation , image segmentation
Summary 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.