Lung carcinogenesis modeling: Resampling and simulation approach to model fitting, validation, and prediction
Doctor of Philosophy thesis
Because of serious health implications, lung cancer is the leading cancer killer for both men and women. It is well known that smoking is the major risk factor for lung cancer. I propose to use a two-stage clonal expansion (TSCE) model to evaluate the effects of smoking on initiation and promotion of lung carcinogenesis. The TSCE model is traditionally fit to prospective cohort data. A new method has been developed that allows reconstruction of cohort data from the combination of risk factor data from a case-control study, and tabled incidence/mortality rate data. A simulation study of the method shows that it is accurate in estimating the parameters of the TSCE model. The method is then applied to fit a TSCE model based on smoking history. The fitted model is then validated in two ways. First the model is used to predict lung cancer deaths in the non-asbestos exposed control arm of the CARET study, where the model predicts 366.8 lung cancer deaths while there were 364 observed. Second, the model is used to simulate LC mortality in the US population and reasonably reproduced observed US mortality rates. The model is also applied to a study of CT screening for lung cancer. The study is a single arm CT screening study lacking a control arm for comparison. The model is used to simulate LC mortality in the absence of screening to serve as a surrogate control arm for comparison. Based on the model there is a statistically significant mortality reduction of 36% due to CT screening.
Statistics; Health sciences; Epidemiology