LiangSCarlinBPGelfandAE. Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information. Ann of App Stat2009; 3: 943–962.
2.
MatherFJChenWVMorganLH. Hierarchical modeling and other spatial analysis in prostate cancer incidence data. Am J Prev Med2006; 30(2S): S88–S100.
3.
ZhouHLawsonABHebertJ. A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer. Stat Med2008; 27: 1468–1489.
4.
ZhangJLawsonA. Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J Appl Statistics2011; 38: 591–603.
5.
BanerjeeSWallMMCarlinBP. Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics2003; 4: 123–142.
6.
MosconeFKnappM. Exploring the spatial pattern of mental health expenditure. J Mental Health Pol and Econ2005; 8: 205–217.
7.
ChengCLChenY-CLiuT-M. Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug prescribing pattern in Taiwan. BMC Public Health2011; 11: 380–380.
8.
NeelonBGhoshPLoebsPF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J Roy Stat Soc2012; 175(4): 1–25.
9.
MobleyLRRootEAnselinL. Spatial analysis of elderly access to primary care services. Int J Health Geog2006; 5: 19–19.
10.
CinnamonJSchuurmanNCrooksVA. A method to determine spatial access to specialized palliative care services using GIS. BMC Health Serv Res2008; 8: 140–140.
