BACKGROUND: As the complexity of microsimulation models increases, concerns about model transparency are heightened. METHODS: The authors conducted model "experiments" to explore the impact of variations in "deep" model parameters using 3 colorectal cancer (CRC) models. All natural history models were calibrated to match observed data on adenoma prevalence and cancer incidence but varied in their underlying specification of the adenocarcinoma process. The authors projected CRC incidence among individuals with an underlying adenoma or preclinical cancer v. those without any underlying condition and examined the impact of removing adenomas. They calculated the percentage of simulated CRC cases arising from adenomas that developed within 10 or 20 years prior to cancer diagnosis and estimated dwell time-defined as the time from the development of an adenoma to symptom-detected cancer in the absence of screening among individuals with a CRC diagnosis. RESULTS: The 20-year CRC incidence among 55-year-old individuals with an adenoma or preclinical cancer was 7 to 75 times greater than in the condition-free group. The removal of all adenomas among the subgroup with an underlying adenoma or cancer resulted in a reduction of 30% to 89% in cumulative incidence. Among CRCs diagnosed at age 65 years, the proportion arising from adenomas formed within 10 years ranged between 4% and 67%. The mean dwell time varied from 10.6 to 25.8 years. CONCLUSIONS: Models that all match observed data on adenoma prevalence and cancer incidence can produce quite different dwell times and very different answers with respect to the effectiveness of interventions. When conducting applied analyses to inform policy, using multiple models provides a sensitivity analysis on key (unobserved) "deep" model parameters and can provide guidance about specific areas in need of additional research and validation.

Context Among patients surgically treated for colon cancer, better survival has been demonstrated in those with more lymph nodes evaluated. The presumed mechanism behind this association suggests that a more extensive lymph node evaluation reduces the risk of understaging, leading to improved survival. Objective To further evaluate the mechanism behind lymph node evaluation and survival by examining the association between more extensive lymph node evaluation, identification of lymph node–positive cancers, and hazard of death. Design Observational cohort study. Setting Surveillance, Epidemiology, and End Results (SEER) program data from 1988 through 2008. Patients 86 394 patients surgically treated for colon cancer. Main Outcome Measure We examined the relationship between lymph node evaluation and node positivity using Cochran-Armitage tests and multivariate logistic regression. The association between lymph node evaluation and hazard of death was evaluated using Cox proportional hazards modeling. Results The number of lymph nodes evaluated increased from 1988 to 2008 but did not result in a significant overall increase in lymph node positivity. During 1988-1990, 34.6% of patients (3875/11 200) had 12 or more lymph nodes evaluated, increasing to 73.6% (9798/13 310) during 2006-2008 (P < .001); however, the proportion of node-positive cancers did not change with time (40% in 1988-1990, 42% in 2006-2008, P = .53). Although patients with high levels of lymph node evaluation were only slightly more likely to be node positive (adjusted odds ratio for 30-39 nodes vs 1-8 nodes, 1.11; 95% CI, 1.02-1.20), these patients experienced significantly lower hazard of death compared with those with fewer nodes evaluated (adjusted hazard ratio for 30-39 nodes vs 1-8 nodes, 0.66; 95% CI, 0.62-0.71; unadjusted 5-year mortality, 35.3%). Conclusion The number of lymph nodes evaluated for colon cancer has markedly increased in the past 2 decades but was not associated with an overall shift toward higher-staged cancers, questioning the upstaging mechanism as the primary basis for improved survival in patients with more lymph nodes evaluated.

BACKGROUND: Microsimulation models are important decision support tools for screening. However, their complexity makes them difficult to understand and limits realization of their full potential. Therefore, it is important to develop documentation that clarifies their structure and assumptions. The authors demonstrate this problem and explore a solution for natural history using 3 independently developed colorectal cancer screening models. METHODS: The authors first project effectiveness and cost-effectiveness of colonoscopy screening for the 3 models (CRC-SPIN, SimCRC, and MISCAN). Next, they provide a conventional presentation of each model, including information on structure and parameter values. Finally, they report the simulated reduction in clinical cancer incidence following a one-time complete removal of adenomas and preclinical cancers for each model. They call this new measure the maximum clinical incidence reduction (MCLIR). RESULTS: Projected effectiveness varies widely across models. For example, estimated mortality reduction for colonoscopy screening every 10 years from age 50 to 80 years, with surveillance in adenoma patients, ranges from 65% to 92%. Given only conventional information, it is difficult to explain these differences, largely because differences in structure make parameter values incomparable. In contrast, the MCLIR clearly shows the impact of model differences on the key feature of natural history, the dwell time of preclinical disease. Dwell times vary from 8 to 25 years across models and help explain differences in projected screening effectiveness. CONCLUSIONS: The authors propose a new measure, the MCLIR, which summarizes the implications for predicted screening effectiveness of differences in natural history assumptions. Including the MCLIR in the standard description of a screening model would improve the transparency of these models.

Background. Clinical trials often report intervention efficacy in terms of the reduction in all-cause