title: Space-time stick-breaking processes for small area disease cluster estimation.
abstract: We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model's ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997-2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.
authors.affiliation: Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
authors.forename: Md Monir
authors.initials: MM
authors.lastname: Hossain
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
authors.forename: Bo
authors.initials: B
authors.lastname: Cai
authors.forename: Jungsoon
authors.initials: J
authors.lastname: Choi
authors.forename: Jihong
authors.initials: J
authors.lastname: Liu
authors.forename: Russell S
authors.initials: RS
authors.lastname: Kirby
keywords: Cluster; Dependence; Dirichlet process mixture; Space-time; Stick-breaking processes