Breast cancer surgery profiling by generalized additive mixed models

The evolution of breast cancer surgery from radical mastectomy to modified radical mastectomy and to breast conservation surgery (BCS) has been due to the verified therapeutic efficacy of BCS with radiation for specific cancer stages, and to the introduction of screening programmes and consequent earlier diagnosis. Despite these facts many patients are still being treated by mastectomy and a great discussion has been related to the use of BCS rates as a measure of quality for comparing breast cancer care between hospitals. To evaluate and compare BCS rates trends among hospitals, one needs to allow for various clinical factors and patient age. Generalized Additive Mixed Models (GAMMs) represent an effective methodological tool to study the probability of undergoing BCS for a given patient, including suitable hospital effects and, at the same time, controlling for individual factors and allowing for a nonlinear age effect. The methodology has been applied to data for 7,045 patients treated in the Friuli Venezia Giulia Region (Italy) from 2001 to 2007. The models were fitted using the Bayesian approach to semiparametric regression, performing a broad array of sensitivity analyses by means of the novel approach of Integrated Nested Laplace Approximation (INLA).