The occupational environment has been a fruitful source of research on causes of cancer. Analyses in studies of occupational risk factors for cancer can experience problems if an attempt is made to model large numbers of exposures, some of which may be highly correlated. Typical analyses of such studies focus on one chemical at a time, but this may not adequately deal with mutual confounding. Based on a large study in Montreal, the objective of this thesis was twofold: to assess several occupational chemicals for their etiologic role in lung cancer, and to explore the use of semi-Bayes modeling to simultaneously estimate the effects of many chemicals at a time. Methods. Data came from a multiple-cancer case-control study of exposures in the work place. The study was comprised of 857 cases of lung cancer and 2172 controls consisting of patients with other types of cancer diagnosed from 1979 to 1985. Detailed occupational histories were collected and occupational hygienists translated these into exposure histories for 231 chemicals. All chemicals were analysed with conventional modeling strategies of both single and multiple parameter models. Of the 231 chemicals, 184 were singled out for analysis in a single large semi-Bayes model, which is a variant of classical empirical Bayes. This analysis is a fairly novel method suited to estimating large numbers of parameters in the face of sparse data. For the Bayesian portion of this model, chemicals were grouped by shared chemical and physical properties, based on the belief that these shared properties would imply similar effects on the risk of lung cancer. Results. Estimates for all 231 chemicals were derived under the various modeling strategies. For most chemicals, estimates changed little across these analytic approaches, though some differences were apparent. Of the 231 chemicals assessed, 53 were earmarked as requiring further evaluation and underwent additional analyses. Discussion. While semi-Bayes models have been shown previously to offer improved estimation over conventional analyses, the gains in using semi-Bayes models in the present study were less clear. Effort put into some portions of the Bayesian modeling did not materially influence the results. A number of chemicals were earmarked as potential lung carcinogens.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.100660 |
| Date | January 2005 |
| Creators | Momoli, Franco G. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Epidemiology and Biostatistics.) |
| Rights | © Franco G. Momoli, 2005 |
| Relation | alephsysno: 002329612, proquestno: AAINR25214, Theses scanned by UMI/ProQuest. |
Page generated in 0.0054 seconds