Spatial smoothing of cancer survival: a Bayesian approach.
A major aim of this paper is to propose and evaluate a method for describing the geographical variation in cancer survival. A fully hierarchical Bayesian approach (FB) which incorporates spatial autocorrelation of the hazard ratios is presented. The method was tried out on data sets of breast cancer and malignant melanoma patients from a population-based cancer registry. The performance of FB was compared with an ordinary Cox proportional hazard method. For both cancers both methods localized some areas of increased and some areas of decreased cancer-specific survival. The estimates provided by the Cox and the FB approach resembled each other, but the FB approach gave more geographical details. In particular, the boundaries of the clusters of high or low survival provided by the FB are more realistic.
Osnes
K
K
Institute for Basic Medical Sciences, Section of Medical Statistics, University of Oslo, P.O. Box 1122 Blindern, N-0317 Oslo, Norway. kaare.osnes@basalmed.uio.no
Aalen
O O
OO
D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000401:mortality; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008545:Melanoma / Q000401:mortality; D008875:Middle Aged; D009369:Neoplasms / Q000401:mortality*; D009664:Norway; D016016:Proportional Hazards Models; D012959:Socioeconomic Factors; D016019:Survival Analysis; D015996:Survival Rate; D013997:Time Factors