Modeling excess lung cancer risk among screened arm participants in the mayo lung project
Article
Goldwasser, DL, Kimmel, M. (2010). Modeling excess lung cancer risk among screened arm participants in the mayo lung project
. CANCER, 116(1), 122-131. 10.1002/cncr.24722
Goldwasser, DL, Kimmel, M. (2010). Modeling excess lung cancer risk among screened arm participants in the mayo lung project
. CANCER, 116(1), 122-131. 10.1002/cncr.24722
BACKGROUND: The Mayo Lung Project (MLP) was a randomized clinical trial designed to test whether periodic screening by chest x-ray reduced lung cancer (LC) mortality in men who were high-risk smokers. Among MLP participants, therewere more deaths from LC in the screening arm both at the trial's end and after long-term follow-up. Overdiagnosis was cited widely as an explanation for the MLP results, whereas a role for excess LC risk attributable to undergoing numerous chest x-ray screenings largely was unexamined. The authors of this report examined the consistency of the MLP data with a modified 2-stage clonal expansion (TSCE) model of excess LC risk. METHODS: By using a simulation model calibrated to the initial MLP data, the authors examined the expected statistical variance of LC incidence and mortality between the screening and control arms. A Bayesian estimation framework using a modified version of the TSCE model to evaluate the role of excess LC risk attributable to chest x-ray screening was derived and applied to the MLP data. RESULTS: Simulation experiments indicated that the overall difference in LC deaths and incidence between the study arm and the control arm was unlikely (P = .0424 and P = .0104, respectively) assuming no excess risk of LC. The authors estimated that the 10-year excess LC risk for a man aged 60 years who smoked and who received 10 chest x-ray screenings was 0.574% (P = .0021). CONCLUSIONS: The excess LC risk observed among screening arm participants was found to be statistically significant with respect to the TSCE model framework in part because of the incorporation of key risk correlates of age and screen frequency into the estimation framework.
