Statistical models for cancer screening

This paper reviews the application of statistical models to planning and evaluating cancer screening programmes. Models used to analyse screening strategies can be classified as either surface models, which consider only those events which can be directly observed such as disease incidence, prevalence or mortality, or deep models, which incorporate hypotheses about the disease process that generates the observed events. This paper focuses on the latter type. These can be further classified as analytic models, which use a model of the disease to derive direct estimates of characteristics of the screening procedure and its consequent benefits, and simulation models, which use the disease model to simulate the course of the disease in a hypothetical population with and without screening and derive measures of the benefit of screening from the simulation outcomes. The main approaches to each type of model are described and an overview given of their historical development and strengths and weaknesses. A brief review of fitting and validating such models is given and finally a discussion of the current state of, and likely future trends in, cancer screening models is presented.