title: Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.
abstract: OBJECTIVE
  Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables.
  
  
  METHODS
  In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina.
  
  
  RESULTS
  Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation.
  
  
  CONCLUSIONS
  Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer.
authors.affiliation: Department of Public Health, Medical University of South Carolina, Charleston. Electronic address: mosra@musc.edu.
authors.forename: Rachel
authors.initials: R
authors.lastname: Carroll
authors.affiliation: Department of Public Health, Medical University of South Carolina, Charleston.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
authors.affiliation: Department of Community and Family Health, University of South Florida, Tampa.
authors.forename: Russell S
authors.initials: RS
authors.lastname: Kirby
authors.affiliation: Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, Agoralaan 1, Diepenbeek, Belgium.
authors.forename: Christel
authors.initials: C
authors.lastname: Faes
authors.affiliation: Department of Public Health, Medical University of South Carolina, Charleston.
authors.forename: Mehreteab
authors.initials: M
authors.lastname: Aregay
authors.affiliation: Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, Agoralaan 1, Diepenbeek, Belgium.
authors.forename: Kevin
authors.initials: K
authors.lastname: Watjou
mesh_terms: D001499:Bayes Theorem; D001984:Bronchial Neoplasms / Q000453:epidemiology* / Q000473:pathology; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000473:pathology; D008297:Male; D015999:Multivariate Analysis; D016012:Poisson Distribution; D015995:Prevalence; D012142:Respiratory Tract Neoplasms / Q000453:epidemiology / Q000473:pathology; D012189:Retrospective Studies; D018570:Risk Assessment; D017062:Small-Area Analysis; D013022:South Carolina / Q000453:epidemiology; D013029:Space-Time Clustering
keywords: Bayesian; Lung and bronchus cancer; MCMC; Mixture modeling; Multivariate; Respiratory cancers; Spatiotemporal