Predicting the Change in Breast Cancer Deaths in Spain by 2019

Abstract

The decrease in the risk of death from breast cancer may exceed the projected increase in deaths from growing population size and aging in Spain. These changes may also explain the decrease in the absolute number of breast cancer deaths in Spain since 2005.

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method

“…In current practice, a subjective prediction base [T * ,T L ] must be defined by the researcher [10] being this choice an assumption of the prediction process [4,5,10,15]. To overcome this, here we propose a procedure, that we call GoF-optimal, which is based on finding the minimum prediction base where the model fits the data.…”

“…Since the model extrapolates the estimated time trend of the disease under study into the future, the choice of both model and prediction base is a challenge for prediction purposes. In this line, it is required to use simple and parsimonious models since predictions made through complex models are not likely to hold true into the future [2,3], unless their use rely on a sustainable evidence [9] such as certain modeling of the period or cohort effect [10], or both [9,11,12].…”

How many new cancer patients in Europe will require radiotherapy by 2025? An ESTRO-HERO analysis

“…Total cancers (n) [a] OUP (%) [b] Optimal radiotherapy courses (n) determinants in the future -the risk profile and diagnostic changes -may be considered sufficiently high as to make reliable predictions of their impact on future trends a difficult and potentially misleading exercise [14].…”

Projecting the future burden of cancer: Bayesian age–period–cohort analysis with integrated nested Laplace approximations

“…(), Clèries et al. (). Bray () provides a comparison of projections derived from linear power models, as well as the classical and Bayesian version of the APC model, and concluded that the Bayesian APC model was the only method to achieve sensible projections.…”

“…Projections based on the APC model are, however, not affected by this problem and hence estimable and interpretable (Holford, 1985). Bayesian APC models are particularly useful to project future cancer burden as they involve no parametric assumptions, see, for example, Berzuini and Clayton (1994), Besag et al (1995), Knorr-Held and Rainer (2001), Dikshit et al (2012), Clèries et al (2013). Bray (2002) provides a comparison of projections derived from linear power models, as well as the classical and Bayesian version of the APC model, and concluded that the Bayesian APC model was the only method to achieve sensible projections.…”