Towards a Multidimensional Approach to Bayesian Disease Mapping

Details

• Alternative Title:
  Bayesian Anal
• Personal Author:
  Martinez-Beneito, Miguel A. ; Botella-Rocamora, Paloma ; Banerjee, Sudipto
• Description:
  Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and non-separable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain).
• Source:
  Bayesian Anal. 12(1):239-259
• Pubmed ID:
  29707101
• Pubmed Central ID:
  PMC5918309
• Document Type:
  Journal Article
• Funding:
  R01 OH010093/OH/NIOSH CDC HHSUnited States/ ; R03 CA179555/CA/NCI NIH HHSUnited States/ ; RC1 GM092400/GM/NIGMS NIH HHSUnited States/
• Volume:
  12
• Issue:
  1
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:d9a2d0c25290fc391cfb8376ee9cb78f5e7230f7268739e5eff3b15c11bdf05b
• Download URL:
  https://stacks.cdc.gov/view/cdc/53773/cdc_53773_DS1.pdf
• File Type:
  [PDF - 774.47 KB ]