title: Examining the association of lung cancer and highly correlated fibre size-specific asbestos exposures with a hierarchical Bayesian model.
abstract: BACKGROUND
  Asbestos is a known carcinogen. However, little is known about the differential effects of size-specific asbestos fibres. Previous research has examined the relationship with lung cancer of each fibre group in the absence of others. Attempts to model all fibre groups within a single regression model have failed due to high correlations across fibre size groups.
  
  
  METHODS
  We compare results from frequentist models for individual fibre size groups, and a hierarchical Bayesian model that included all fibre groups to estimate the relationship of size-specific asbestos fibre groups to lung cancer mortality. The hierarchical model assumes partial exchangeability of the effects of size-specific asbestos fibre groups to lung cancer, and is capable of handling the strong correlation of the exposure data.
  
  
  RESULTS
  When fibre groups are modelled independently with a frequentist model, there appears to be an increase in the dose-response with increasing fibre size. However, when subject to a hierarchical structure, this trend vanishes, and the effects of distinct fibre groups appear largely similar.
  
  
  CONCLUSIONS
  This is the first occasion where distinct asbestos fibre groups have been assessed in a single regression model; however, even the use of a hierarchical modelling structure does not appear to overcome all the statistical fluctuations arising from the high correlations across fibre groups. We believe these results should be compared with other occupational cohorts with similar fibre group information. Finally, results for the smallest fibre group may be suggestive of a carcinogenic potential for nanofibres.
authors.affiliation: International Agency for Research on Cancer, Lyon, France.
authors.forename: Ghassan B
authors.initials: GB
authors.lastname: Hamra
authors.forename: Dana
authors.initials: D
authors.lastname: Loomis
authors.forename: John
authors.initials: J
authors.lastname: Dement
mesh_terms: D001194:Asbestos / Q000009:adverse effects*; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008168:Lung / Q000187:drug effects*; D008175:Lung Neoplasms / Q000139:chemically induced*; D008297:Male; D019016:Mineral Fibers / Q000009:adverse effects; D057139:Nanofibers / Q000009:adverse effects; D009784:Occupational Diseases / Q000139:chemically induced*; D016273:Occupational Exposure / Q000009:adverse effects* / Q000032:analysis; D010316:Particle Size; D013783:Textile Industry
chemical_list: D019016:Mineral Fibers; D001194:Asbestos
keywords: Bayesian; asbestos; hierarchical modeling; occupational epidemiology