Browsing by Author "Foy, Millennia"

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    Lung carcinogenesis modeling: Resampling and simulation approach to model fitting, validation, and prediction
    (2010) Foy, Millennia; Kimmel, Marek
    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.
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    Modeling the Natural History and Detection of Lung Cancer Based on Smoking Behavior
    (Public Library of Science, 2014) Chen, Xing; Foy, Millennia; Kimmel, Marek; Gorlova, Olga Y.
    In this study, we developed a method for modeling the progression and detection of lung cancer based on the smoking behavior at an individual level. The model allows obtaining the characteristics of lung cancer in a population at the time of diagnosis. Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were used to fit the lung cancer progression and detection model. The fitted model combined with a smoking based carcinogenesis model was used to predict the distribution of age, gender, tumor size, disease stage and smoking status at diagnosis and the results were validated against independent data from the SEER database collected from 1988 to 1999. The model accurately predicted the gender distribution and median age of LC patients of diagnosis, and reasonably predicted the joint tumor size and disease stage distribution.