Recalibrating the Lung Cancer Policy Model to Address Lung Cancer Risk and Lung Cancer Screening Practices in Real-World Populations

We previously developed the Lung Cancer Policy Model (LCPM) to evaluate the effectiveness and cost-effectiveness of lung cancer screening with low-dose computed tomography (LDCT). The value of the LCPM lies in its ability to estimate the health and economic consequences of LDCT screening at the population level; however, certain assumptions in the model may not reflect “real-world” conditions and therefore may lead to inaccurate and potentially misleading estimates of the impact of LDCT screening. The goal of the proposed research is to recalibrate the LCPM to address two key limitations of the current LCPM and use the recalibrated model to assess the effectiveness and cost-effectiveness of different lung cancer screening scenarios under real-world conditions. Presently, the LCPM makes two key assumptions that may not accurately reflect real-world conditions. First, the LCPM assumes 100% adoption of LDCT screening among eligible individuals; this assumption, while commonly used in screening models does not accurately represent the real-world, where fewer than 6% of eligible individuals currently undergo screening. Second, the LCPM makes several assumptions about the relationships between different smoking parameters and lung cancer risk. However, the LCPM was calibrated using data from cohorts in which nearly all participants (with a smoking history) smoked > 20 cigarettes per day. Consequently, the LCPM may not accurately model lung cancer risk among individuals who smoke fewer cigarettes per day, a population that is growing in the U.S. The proposed research will address these two key limitations of the current LCPM by 1) recalibrating the model using data on both smoking duration and smoking intensity from two cohorts that include individuals who smoke at lower intensities and 2) incorporating the screening adoption rate as a parameter in the model. We will first develop a calibration package to inform the model and will use it to recalibrate the model. We will then use the recalibrated model to assess the effectiveness and cost-effectiveness of different screening strategies. The proposed research is highly innovative as it will recalibrate the LCPM to allow for more accurate estimates of lung cancer risk and will enable the investigation of the benefits and harms of lung cancer screening under real-world conditions. This research will move beyond traditional lung cancer screening modeling approaches, which have largely focused on modeling the theoretical benefits, harms, and costs of lung cancer screening under ideal conditions. Importantly, the findings generated from this research can be used to inform revisions to the USPSTF lung cancer screening guideline to improve the selection of high-risk individuals for screening.