Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project

Using a micro-simulation model, the MISCAN-lung model, we explore the possible influence of screening sensitivity, systematic error, over-diagnosis and population heterogeneity.Calibrating screening sensitivity, systematic error, or over-diagnosis does not noticeably improve the fit of the model, whereas calibrating population heterogeneity helps the model predict lung cancer incidence better.Our conclusion is that the hypothesized imperfection in screening sensitivity, systematic error, and over-diagnosis do not in themselves explain the observed trial results. Model fit improvement achieved by accounting for population heterogeneity suggests a higher risk of cancer incidence in the intervention group as compared with the control group.Lung cancer is the leading cause of cancer deaths among men and women in the United States [1]. Clinical treatment has not led to major improvements in patients' survival from lung cancer. Although steps are being taken to prevent lung cancer incidence, particularly through anti-tobacco public health efforts, considerable time is required to realize a noticeable decrease in the population incidence of this disease. As a result, considerable attention has focused on the utility of screening as a strategy for achieving a reduction in lung cancer mortality.The Mayo Lung Project (MLP), a randomized controlled trial including 9211 male cigarette smokers, was conducted between 1971 and 1983 to assess whether frequent screening through chest x-rays and sputum cytology administered every four months for a six-year period would result in a long-term reduction in lung cancer mortality. Control group participants received usual care at the Mayo Clinic, i.e., advice to receive these screening tests annually. No significant between-group difference was found in lung cancer mortality, at the end of the trial as well as at the end of an extended 20 year follow-up [2], when lung cancer mortality rates were defined as the number of lung cancer deaths