Projecting cancer prevalence through combining age-period-cohort models and survival models

The number of people living with a diagnosis of cancer has increased over the last decades due to increasing incidence, improved survival, population growth and a shifting age distribution. Projections of cancer incidence and prevalence are used to estimate the future burden of cancer and help inform future resource requirements. Prevalence projections can be estimated by combining statistical models to predict future incidence and future survival, alongside estimates of future population structure. I will describe an approach, together with new commands, to predict future cancer prevalence that combines age-period-cohort (APC) models incorporating natural splines and flexible parametric survival models. For the APC models different assumptions about future incidence rates can be made through including various combinations of different link functions, moving the upper boundary knot of the spline function and 'dampening' future incidence. Different approaches to extrapolating future survival can also be made through use of period analysis and/or careful modeling of calendar time. As with any extrapolations a well-informed sensitivity analysis is vital, as well as investigation of realistic "what if?" scenarios. I will describe new Stata commands to fit the APC models (apcmodel) and a set of postestimation commands to predict future incidence to combine the APC model with a survival model fitted using stpm3.