File S1 - Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling

Supporting figures and tables. Figure S1, Prevalence of smoking by age in 1950 birth cohort. Summary of shared input data (used by all 5 models) on smoking patterns for the US cohort born in 1950. Prevalence shown is estimated in the absence of lung cancer mortality. Version 1.0 of the Smoking History Generator (SHG) refers to published data through 2000 (Anderson, et al.), and version 1.5 supplies the 1950 birth cohort used for this analysis with data through 2009 and projections past 2009. Figure S2, Other-cause mortality, by smoking quintile, in 1950 birth cohort. These curves show the other-cause (non-lung cancer) mortality for never smokers and for current smokers by smoking quintile (Q, of cigarettes per day) for the male birth cohort of 1950, out to age 99. Former smokers are intermediate to current and never smokers. There is a similar plot for females. These were shared inputs used by all the models. Note that the rates of non-lung cancer mortality represent the US population, not trial (NLST or PLCO) participants. Figure S3, Prevalence of smoking by age in 1950 birth cohort. Output from one model showing smoking prevalence by age (calendar year), in a no screening scenario. Proportions of current/former/never smokers are in the presence of lung cancer mortality as well as all-cause mortality. Figure S4, Prevalence of smoking by age and pack-years in 1950 birth cohort. Output from one model showing smoking prevalence by category of pack-year and age. The proportion of the cohort by age that has accumulated the specified number of pack-years in the presence of lung cancer mortality and other-cause mortality. Figure S5, Incidence, no screening scenario, output from all models. For predictions past observed SEER data (over age 60) there are no observed data, but we used an age-period-cohort model to project past observed years (‘Projected’ red double line in plots below), which shows that the models are most divergent after age 85, when SEER data become most sparse. We cannot strictly compare incidence to that in prior birth cohorts since smoking patterns are dissimilar, and incidence varies by cohort. Figure S6, Mortality, no screening scenario, output from all models. The vertical line at age 90 indicates age at which all event counts (screens, deaths and deaths averted, and life years gained) were truncated for the analyses reported here. Although the models ranked programs similarly, there was variability in the total numbers of predicted lung cancer cases, deaths, and therefore lung cancer deaths prevented. The differences in rates in the no screening scenario in large part explains the predicted differences between models. The four models (E, F, S, and U) which use two-stage or multi-stage clonal expansion models have more similarly shaped curves than the fifth model (M), which does not use a clonal expansion component (see Table S1 in File S1). Figure S7, Results from all models analogous to Figure 1 in article. Figure S8, Results from all models analogous to Figure 2 in article. Figure S9, Secondary results with reduced operative candidacy with age. The dashed line denotes the efficiency frontier in the main analysis. Table S1, Additional Detail on Models. Table S2, Complete List of 120 Consensus Efficient Scenarios. Table S3, Comparison of Consensus Efficient Scenarios Identified Using Life-years Saved or Lung Cancer Deaths Avoided as Measure of Benefit.
(DOCX)