Modeling in Colorectal Cancer Screening: Assessing External and Predictive Validity of MISCAN-Colon Microsimulation Model Using NORCCAP Trial Results
Medical Decision Making: an international journal , Volume 38 - Issue 8 p. 917- 929
BACKGROUND: Microsimulation models are increasingly being used to inform colorectal cancer (CRC) screening recommendations. MISCAN-Colon is an example of such a model, used to inform the Dutch CRC screening program and US Preventive Services Task Force guidelines. Assessing the validity of these models is essential to provide transparency regarding their performance. In this study, we tested the external and predictive validity of MISCAN-Colon. METHODS: We validated MISCAN-Colon using the Norwegian Colorectal Cancer Prevention (NORCCAP) trial, a randomized controlled trial that examined the effectiveness of once-only flexible sigmoidoscopy (FS) screening. We simulated the study population and design of the NORCCAP trial in MISCAN-Colon and compared 10- to 12-year model-predicted hazard ratios (HRs) for overall and distal CRC incidence and mortality to those observed. In addition, we compared the numbers of screen-detected neoplasia. Finally, we predicted the trial's future results to allow for the assessment of predictive validity. RESULTS: MISCAN-Colon predicted an HR for overall CRC incidence (0.85), distal CRC incidence (0.82), overall CRC mortality (0.68), and distal CRC mortality (0.62). These were within the limits of the 95% confidence intervals of the NORCCAP trial results. Similar results were observed for the number of screen-detected cancers. The model significantly underestimated the number of screen-detected adenomas. Model-predicted HRs for CRC incidence and mortality up to 15- to 17-year follow-up were 0.84 and 0.72, respectively. CONCLUSION: Although the underestimation of screen-detected adenomas requires further investigation, MISCAN-Colon is able to make a valid replication of the CRC incidence and mortality reduction of an FS screening trial, which suggests that it can be considered a useful tool to support decision making on CRC screening.