Despite large increases in the uptake of screening in the past two decades, colorectal cancer (CRC) is still the second leading cause of cancer death in the US. This points to inadequate screening and treatment, and gaps in care that need to be addressed. This project will use leadership-class computing resources to run comparative probabilistic sensitivity analyses (PSAs) of screening strategies with three state-of-the-art CRC models.
Technological advances are bringing new methods for risk-targeted screening and treatment and new screening modalities. There is a critical need to assess these potential improvements in terms of both their ability to reduce the burden of CRC and their associated costs. But it is not logistically or ethically feasible to conduct clinical trials of all possible interventions. Instead, computational models, in the form of natural history microsimulations, are used as in silico laboratories to evaluate the potential impact of changes in clinical practice and new policies on clinical and economic CRC outcomes. These models are based on information about underlying disease process, sensitivity and specificity of screening tests, and treatment effectiveness. There is uncertainty in both available data, which is observed with error, and the models, which describe unobservable processes. In the face of these uncertainties, large-scale computation is required to provide robust evidence for effective screening approaches.
Funded under the National Cancer Institute’s (NCI) Cancer Intervention and Surveillance Modeling Network (CISNET) program, these models were independently developed for the evaluation of interventions, with emphasis on screening, and describe CRC natural history using different underlying assumptions. Building on model calibration, comparison, and evaluation of screening efficacy that was accomplished in the 2021-2022 period, the project will extend analyses to extensions of the microsimulation models that will incorporate the serrated pathway to colorectal cancer. Research examining the serrated pathway has been thwarted by uncertainty in this process, which will be addressed by incorporating additional sources and combinations of uncertainty into simulations to examine their impacts on the projected results, including its effects on fecal immunochemical test (FIT) screening. The comparative PSAs in this work will be used to generate cost-effectiveness analyses for complex interventions and to provide formalized assessments of uncertainties across the three CRC models. This work integrates complex data, large-scale machine learning algorithms for uncertainty quantification, and simulation to advance HPC-enabled scientific discovery.