Age, period and cohort effects in Bayesian smoothing of spatial cancer survival with geoadditive models.
Based on the example of data on breast cancer survival in a specific area in France, this paper describes a Bayesian approach to analysing individual continuous-time spatially referenced survival data. Starting from the well-known Cox model we develop a geoadditive survival model with a baseline effect, spatial effects, age, period and cohort effects as well as the effect of the number of metastases at the time of diagnosis. Furthermore, we also investigate temporal and spatial variations in the effect of the number of metastases. Our approach is particularly useful since we find clear hints for a violation of the proportional hazards assumption and the existence of different spatial patterns for patients with no, one and more than one metastasis. The reliability of our approach is attested by comparison with a parametric model and with several simulated data sets with known risk profiles.
Sauleau
Erik A
EA
Cancer Registry of Haut-Rhin, Mulhouse, France. sauleauea@ch-mulhouse.fr
Hennerfeind
Andrea
A
Buemi
Antoine
A
Held
Leonhard
L
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001699:Biometry; D001943:Breast Neoplasms / Q000401:mortality / Q000556:secondary; D015331:Cohort Studies; D016208:Databases, Factual; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality*; D016016:Proportional Hazards Models; D012042:Registries; D016019:Survival Analysis