Evaluations of cancer screening programs tend to focus on the early detection of cancer, but assessing the value of cancer screening requires the estimation of the consequences of early detection. Such consequences include improved health outcomes and reduced downstream costs, but also potentially increased costs due to the over treatment of precancerous lesions that would not have affected patients within their remaining lifetime. It is generally not feasible to conduct clinical trials that are able to detect such long-term consequences and so decision analytic modelling methods are commonly used to predict expected costs and patient outcomes. Such models also allow the comparison of the costs and effects of alternative potential screening programs to a no screening scenario.
This talk will introduce the types of data and modelling methods used to assess the value of cancer screening, using applied cancer screening models to illustrate the methods and model outputs and their use to inform funding decisions.