title: A hierarchical aggregate data model with spatially correlated disease rates.
abstract: The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model.
authors.affiliation: Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, Seattle, Washington 98109, USA. kguthrie@fhcrc.org
authors.forename: Katherine A
authors.initials: KA
authors.lastname: Guthrie
authors.forename: Lianne
authors.initials: L
authors.lastname: Sheppard
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
mesh_terms: D001499:Bayes Theorem; D002908:Chronic Disease; D003198:Computer Simulation; D004781:Environmental Exposure / Q000009:adverse effects; D015340:Epidemiologic Research Design; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D035583:Rare Diseases / Q000453:epidemiology*