Bayesian hierarchical modeling to account for complex patterns of measurement error in cohort studies. Application in radiation epidemiology.
Résumé
Despite its deleterious consequences for statistical inference and its ubiquity in observational
research, exposure measurement error is rarely accounted for in epidemiological studies. Basically,
standard correction methods, like regression calibration or SIMEX, often lack the flexibility to account
for complex patterns of exposure uncertainty. However, in occupational cohort studies, for instance,
changes in the methods of exposure assessment can lead to complex error structures. Moreover, a
strategy of group-exposure assessment and individual worker characteristics may lead to error
components that are shared between or within individuals. In this work, we thus propose and fit several
Bayesian hierarchical models, combining survival models with time-dependent covariates,
measurement and exposure models, to obtain a corrected estimate ofthe potential association between
exposure to radon and mortality for several cancer types in the French cohort of uranium miners. A
simulation study is under progress to assess the impact of model misspecification on risk estimates.
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Licence : Copyright (Tous droits réservés)
Licence : Copyright (Tous droits réservés)