title: Prior choice in discrete latent modeling of spatially referenced cancer survival.
abstract: In this article, we examine the development and use of covariate models where the relation with explanantory covariates is spatially adaptive. In this way space is regarded as an effect modifier. We examine the possibility of discrete groupings of coefficients (clustering of coefficients). Our application is to prostate cancer survival based on the SEER cancer registry for the state of Louisiana, USA. This registry holds individual records linked to vital outcomes and is geo-coded at county level. We examine a range of potential prior distributions for groupings of regression coefficients in application to these data.
authors.affiliation: 1Division of Biostatistics & Epidemiology, College of Medicine, Medical University of South Carolina, UK.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
authors.forename: Jungsoon
authors.initials: J
authors.lastname: Choi
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D056808:Biostatistics; D006801:Humans; D053208:Kaplan-Meier Estimate; D016014:Linear Models; D008145:Louisiana / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality*; D011471:Prostatic Neoplasms / Q000401:mortality; D012044:Regression Analysis; D018426:SEER Program / Q000706:statistics & numerical data; D017062:Small-Area Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis