An evidence-based microsimulation model for colorectal cancer: validation and application

Black-White disparities in colorectal cancer outcomes: a simulation study of screening benefit

The US Black population has higher colorectal cancer (CRC) incidence rates and worse CRC survival than the US White population, as well as historically lower rates of CRC screening. The Surveillance, Epidemiology, and End Results incidence rate data in people diagnosed between the ages of 20 and 45 years, before routine CRC screening is recommended, were analyzed to estimate temporal changes in CRC risk in Black and White populations. There was a rapid rise in rectal and distal colon cancer incidence in the White population but not the Black population, and little change in proximal colon cancer incidence for both groups. In 2014-2018, CRC incidence per 100 000 was 17.5 (95% confidence interval [CI] = 15.3 to 19.9) among Black individuals aged 40-44 years and 16.6 (95% CI = 15.6 to 17.6) among White individuals aged 40-44 years; 42.3% of CRCs diagnosed in Black patients were proximal colon cancer, and 41.1% of CRCs diagnosed in White patients were rectal cancer. Analyses used a race-specific microsimulation model to project screening benefits, based on life-years gained and lifetime reduction in CRC incidence, assuming these Black-White differences in CRC risk and location. The projected benefits of screening (via either colonoscopy or fecal immunochemical testing) were greater in the Black population, suggesting that observed Black-White differences in CRC incidence are not driven by differences in risk. Projected screening benefits were sensitive to survival assumptions made for Black populations. Building racial disparities in survival into the model reduced projected screening benefits, which can bias policy decisions.

Cost-effectiveness of preventive aspirin use and intensive downstaging polypectomy in patients with familial adenomatous polyposis: A microsimulation modeling study

OBJECTIVE

Although there is increasing evidence to suggest the cost-effectiveness of aspirin use to prevent colorectal cancer (CRC) in the general population, no study has assessed cost-effectiveness in patients with familial adenomatous polyposis (FAP), who are at high risk of developing CRC. We examined the cost-effectiveness of preventive use of low-dose aspirin in FAP patients who had undergone polypectomy in comparison with current treatment practice.

DESIGN

We developed a microsimulation model that simulates a hypothetical cohort of the Japanese population with FAP for 40 years. Three scenarios were created based on three intervention strategies for comparison with no intervention, namely intensive downstaging polypectomy (IDP) of colorectal polyps at least 5.0 mm in diameter, IDP combined with low-dose aspirin, and total proctocolectomy with ileal pouch-anal anastomosis (IPAA). Cost-effective strategies were identified using a willingness-to-pay threshold of USD 50,000 per QALY gained.

RESULTS

Compared with no intervention, all strategies resulted in extended QALYs (21.01-21.43 QALYs per individual) and showed considerably reduced colorectal cancer mortality (23.35-53.62 CRC deaths per 1000 individuals). Based on the willingness-to-pay threshold, IDP with low-dose aspirin was more cost-effective than the other strategies, with an incremental cost-effectiveness ratio of $57 compared with no preventive intervention. These findings were confirmed in both one-way sensitivity analyses and probabilistic sensitivity analyses.

CONCLUSION

This study suggests that the strategy of low-dose aspirin with IDP may be cost-effective compared with IDP-only or IPAA under the national fee schedule of Japan.

Comparison of Simulated Outcomes Between Stool- and Blood-Based Colorectal Cancer Screening Tests

The Centers for Medicare & Medicaid Services (CMS) recommend covering blood-based tests meeting proposed minimum performance thresholds for colorectal cancer (CRC) screening. Outcomes were compared between currently available stool-based screening tests and a hypothetical blood-based test meeting CMS minimum thresholds. Using the Colorectal Cancer and Adenoma Incidence and Mortality Microsimulation Model (CRC-AIM), outcomes were simulated for average-risk individuals screened between ages 45 and 75 years with triennial multitarget stool DNA (mt-sDNA), annual fecal immunochemical test (FIT), and annual fecal occult blood test (FOBT). Per CMS guidance, blood-based CRC screening was modeled triennially, with 74% CRC sensitivity and 90% specificity. Although not specified by CMS, adenoma sensitivity was set between 10% and 20%. Published adenoma and CRC sensitivity and specificity were used for stool-based tests. Adherence was set at (1) 100%, (2) 30%-70%, in 10% increments, and (3) real-world rates for stool-based tests (mt-sDNA = 65.6%; FIT = 42.6%; FOBT = 34.4%). Assuming perfect adherence, a blood-based test produced ≥19 lower life-years gained (LYG) than stool-based strategies. At the best-case scenario for blood-based tests (100% adherence and 20% adenoma sensitivity), mt-sDNA at real-world adherence achieved more LYG (287.2 vs. 297.1, respectively) with 14% fewer colonoscopies. At 100% blood-based test adherence and real-world mt-sDNA and FIT adherence, the blood-based test would require advanced adenoma sensitivity of 30% to reach the LYG of mt-sDNA (297.1) and ∼15% sensitivity to reach the LYG of FIT (258.9). This model suggests that blood-based tests with CMS minimally acceptable CRC sensitivity and low advanced adenoma sensitivity will frequently yield inferior outcomes to stool-based testing across a wide range of adherence assumptions.

Robustness Analysis of Colorectal Cancer Colonoscopy Screening Strategies

Colorectal Cancer (CRC) is a leading cause of cancer deaths in the United States. Despite significant overall declines in CRC incidence and mortality, there has been an alarming increase in CRC among people younger than 50. This study uses an established microsimulation model, CRC-SPIN, to perform a 'stress test' of colonoscopy screening strategies. First, we expand CRC-SPIN to include birth-cohort effects. Second, we estimate natural history model parameters via Incremental Mixture Approximate Bayesian Computation (IMABC) for two model versions to characterize uncertainty while accounting for increased early CRC onset. Third, we simulate 26 colonoscopy screening strategies across the posterior distribution of estimated model parameters, assuming four different colonoscopy sensitivities (104 total scenarios). We find that model projections of screening benefit are highly dependent on natural history and test sensitivity assumptions, but in this stress test, the policy recommendations are robust to the uncertainties considered.

The NCC mathematical modeling framework for decision-making of six major cancers

Objective

Mathematical modeling and simulation is a useful research method to inform decision-making. This article aims to describe the National Cancer Center (NCC) modeling framework and how well it reproduces observed empirical data for six major cancers.

Methods

We developed the NCC modeling framework for six major cancers in China (lung, liver, stomach, colorectal, esophageal, and breast), which simulates the life-histories represented by states among normal, precancerous lesion, stage-specific invasive cancer, and death for six cancers separately. Each NCC simulation model could be illustrated as an integrated framework of 3 modules: a demography module, natural history module, and screening module. Combined with costs and health utilities data, the models could have many detailed outputs for informing decisions, including the harm of screening (e.g., false positives, complications, and overdiagnosis), healthcare costs, and benefits (quality-adjusted life years gained, cancer incidence and mortality, and investment returns). We calibrated the models to Chinese population-based observations on cancer incidence, mortality, and stage distribution. All models are validated by comparing model simulated results to data observed from nationwide cancer registration and a large prospective cohort study.

Results

The simulated results from the calibrated models consistently match the epidemiological patterns in six major cancer incidence, mortality, and stage distributions in China. Model projected age-specific cancer incidence and mortality were close to the observed data in the national cancer registration. The NCC modeling framework reproduced the cumulative cancer cases and deaths observed in the prospective cohort study at 7.0 and 10.8 years of follow-up. Model estimated net survival rates also consistent with population-based statistics.

Conclusion

The NCC modeling framework's ability to reproduce the observed population-level cancer statistics and the cancer cases in a prospective cohort study suggests its results are reliable to inform decision-making related to six major cancers in China.

Coupé VM. A progressive three-state model to estimate time to cancer: a likelihood-based approach

Background

To optimize colorectal cancer (CRC) screening and surveillance, information regarding the time-dependent risk of advanced adenomas (AA) to develop into CRC is crucial. However, since AA are removed after diagnosis, the time from AA to CRC cannot be observed in an ethically acceptable manner. We propose a statistical method to indirectly infer this time in a progressive three-state disease model using surveillance data.

Methods

Sixteen models were specified, with and without covariates. Parameters of the parametric time-to-event distributions from the adenoma-free state (AF) to AA and from AA to CRC were estimated simultaneously, by maximizing the likelihood function. Model performance was assessed via simulation. The methodology was applied to a random sample of 878 individuals from a Norwegian adenoma cohort.

Results

Estimates of the parameters of the time distributions are consistent and the 95% confidence intervals (CIs) have good coverage. For the Norwegian sample (AF: 78%, AA: 20%, CRC: 2%), a Weibull model for both transition times was selected as the final model based on information criteria. The mean time among those who have made the transition to CRC since AA onset within 50 years was estimated to be 4.80 years (95% CI: 0; 7.61). The 5-year and 10-year cumulative incidence of CRC from AA was 13.8% (95% CI: 7.8%;23.8%) and 15.4% (95% CI: 8.2%;34.0%), respectively.

Conclusions

The time-dependent risk from AA to CRC is crucial to explain differences in the outcomes of microsimulation models used for the optimization of CRC prevention. Our method allows for improving models by the inclusion of data-driven time distributions.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01645-2).

When History and Heterogeneity Matter: A Tutorial on the Impact of Markov Model Specifications in the Context of Colorectal Cancer Screening

BACKGROUND

Markov models are used in health research to simulate health care utilization and disease states over time. Health phenomena, however, are complex, and the memoryless assumption of Markov models may not appropriately represent reality. This tutorial provides guidance on the use of Markov models of different orders and stratification levels in health decision-analytic modeling. Colorectal cancer (CRC) screening is used as a case example to examine the impact of using different Markov modeling approaches on CRC outcomes.

METHODS

This study used insurance claims data from commercially insured individuals in Oregon to estimate transition probabilities between CRC screening states (no screen, colonoscopy, fecal immunochemical test or fecal occult blood test). First-order, first-order stratified by sex and geography, and third-order Markov models were compared. Screening trajectories produced from the different Markov models were incorporated into a microsimulation model that simulated the natural history of CRC disease progression. Simulation outcomes (e.g., future screening choices, CRC incidence, deaths due to CRC) were compared across models.

RESULTS

Simulated CRC screening trajectories and resulting CRC outcomes varied depending on the Markov modeling approach used. For example, when using the first-order, first-order stratified, and third-order Markov models, 30%, 31%, and 44% of individuals used colonoscopy as their only screening modality, respectively. Screening trajectories based on the third-order Markov model predicted that a higher percentage of individuals were up-to-date with CRC screening as compared with the other Markov models.

LIMITATIONS

The study was limited to insurance claims data spanning 5 y. It was not possible to validate which Markov model better predicts long-term screening behavior and outcomes.

CONCLUSIONS

Findings demonstrate the impact that different order and stratification assumptions can have in decision-analytic models.

HIGHLIGHTS

This tutorial uses colorectal cancer screening as a case example to provide guidance on the use of Markov models of different orders and stratification levels in health decision-analytic models.Colorectal cancer screening trajectories and projected health outcomes were sensitive to the use of alternate Markov model specifications.Although data limitations precluded the assessment of model accuracy beyond a 5-y period, within the 5-y period, the third-order Markov model was slightly more accurate in predicting the fifth colorectal cancer screening action than the first-order Markov model.Findings from this tutorial demonstrate the importance of examining the memoryless assumption of the first-order Markov model when simulating health care utilization over time.

Microsimulation Model for Prevention and Intervention of Coloretal Cancer in China (MIMIC-CRC): Development, Calibration, Validation, and Application

Introduction

A microsimulation model provides important references for decision-making regarding colorectal cancer (CRC) prevention strategies, yet such a well-validated model is scarce in China.

Methods

We comprehensively introduce the development of MIcrosimulation Model for the prevention and Intervention of Colorectal Cancer in China (MIMIC-CRC). The MIMIC-CRC was first constructed to simulate the natural history of CRC based on the adenoma-carcinoma pathway. The parameters were calibrated and validated using data from population-based cancer registry data and CRC screening programs. Furthermore, to assess the model’s external validity, we compared the model-derived results to outcome patterns of a sigmoidoscopy screening trial in the UK [UK Flexible Sigmoidoscopy Screening (UKFSS) trial]. Finally, we evaluated the application potential of the MIMIC-CRC model in CRC screening by comparing the 8 different strategies.

Results

We found that most of the model-predicted colorectal lesion prevalence was within the 95% CIs of observed prevalence in a large population-based CRC screening program in China. In addition, model-predicted sex- and age-specific CRC incidence and mortality were equivalent to the registry-based data. The hazard ratios of model-estimated CRC-related incidence and mortality for sigmoidoscopy screening compared to no screening were 0.60 and 0.51, respectively, which were comparable to the reported results of the UKFSS trial. Moreover, we found that all 8 strategies could reduce CRC incidence and mortality compared to no screening.

Conclusions

The well-calibrated and validated MIMIC-CRC model may represent a valid tool to assess the comparative effectiveness of CRC screening strategies and will be useful for further decision-making to CRC prevention.

Cancer Modeling as Learning Experience

Cancer modeling has become an accepted method for generating evidence about comparative effectiveness and cost-effectiveness of candidate cancer control policies across the continuum of care. Models of early detection policies require inputs concerning disease natural history and screening test performance, which are often subject to considerable uncertainty. Model validation against an external data source can increase confidence in the reliability of assumed or calibrated inputs. When a model fails to validate, this presents an opportunity to revise these inputs, thereby learning new information about disease natural history or diagnostic performance that could both enhance the model results and inform real-world practices. We discuss the conditions necessary for validly drawing conclusions about specific inputs such as diagnostic performance from model validation studies. Doing so requires being able to faithfully replicate the validation study in terms of its design and implementation and being alert to the problem of non-identifiability, which could lead to explanations for failure to validate other than those identified. See related article by Rutter et al., p. 775.

An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

BACKGROUND

Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history.

METHODS

We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments.

RESULTS

In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations.

DISCUSSION

The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

BACKGROUND

Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive.

METHODS

This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low.

RESULTS

The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity.

CONCLUSIONS

A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

BACKGROUND

Models can help guide colorectal cancer (CRC) screening policy. While models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance.

METHODS

We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6mm), 0.85 for small adenomas (6-10mm), 0.95 for large adenomas ( {greater than or equal to} 10mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. We examined how well the model predicted outcomes measured over a three-year follow-up period, including: the number of adenomas detected, the size of the largest adenoma detected, and incident CRC.

RESULTS

Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%-50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%-17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10-20mm adenomas, and 0.98 for adenomas 20mm and larger.

CONCLUSIONS

Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations.

IMPACT

Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may impact the predicted effectiveness of CRC screening. When possible, decision analyses used to inform policy should address these uncertainties.

Estimation of overdiagnosis in colorectal cancer screening with sigmoidoscopy and faecal occult blood testing: comparison of simulation models

OBJECTIVE

To estimate overdiagnosis of colorectal cancer (CRC) for screening with sigmoidoscopy and faecal occult blood testing (FOBT).

DESIGN

Simulation study using data from randomised trials.

SETTING

Primary screening, UK, Norway PARTICIPANTS: 152 850 individuals from the Nottingham trial and 98 678 individuals from the Norwegian Colorectal Cancer Prevention (NORCCAP) trial.

INTERVENTION

CRC screening.

OUTCOME MEASURE

We estimated overdiagnosis using long-term data from two randomised trials: the Nottingham trial comparing FOBT screening every other year to no-screening, and the NORCCAP trial comparing once-only sigmoidoscopy screening to no-screening. To estimate the natural growth of adenomas to CRC, we used the following microsimulation models: (i) the Microsimulation Screening Analysis; (ii) the CRC Simulated Population model for Incidence and Natural history; (iii) the Simulation Model of Colorectal Cancer; (iv) a model derived by the German Cancer Research Center. We defined overdiagnosed cancers as the difference between the observed number of CRCs in the no-screening arm and the expected number of cancers in screening arm (sum of observed and prevented by adenoma removal). The amount of overdiagnosis is defined as the number of overdiagnosed cancers over the number of cancers observed in the no-screening arm.

RESULTS

Overdiagnosis estimates were highly dependent on model assumptions. For FOBT screening with 2354 cancers observed in control arm, four out of five models predicted overdiagnosis, range 2.0% (2400 cancers expected in screening) to 7.6% (2533 cancers expected in screening). For sigmoidoscopy screening with 452 cancers observed in control arm, all models predicted overdiagnosis, range 25.2% (566 cancers expected in screening) to 128.1% (1031 cancers expected in screening).

CONCLUSIONS

The amount of overdiagnosis estimated based on the microsimulation models varied substantially. Microsimulation models may not give reliable estimates of the preventive effect of adenoma removal, and should be used with caution to inform guidelines.

Impact of screening and follow-up colonoscopy adenoma sensitivity on colorectal cancer screening outcomes in the CRC-AIM microsimulation model

BACKGROUND

Real-world data for patients with positive colorectal cancer (CRC) screening stool-tests demonstrate that adenoma detection rates are lower when endoscopists are blinded to the stool-test results. This suggests adenoma sensitivity may be lower for screening colonoscopy than for follow-up to a known positive stool-based test. Previous CRC microsimulation models assume identical sensitivities between screening and follow-up colonoscopies after positive stool-tests. The Colorectal Cancer and Adenoma Incidence and Mortality Microsimulation Model (CRC-AIM) was used to explore the impact on screening outcomes when assuming different adenoma sensitivity between screening and combined follow-up/surveillance colonoscopies.

METHODS

Modeled screening strategies included colonoscopy every 10 years, triennial multitarget stool DNA (mt-sDNA), or annual fecal immunochemical test (FIT) from 50 to 75 years. Outcomes were reported per 1000 individuals without diagnosed CRC at age 40. Base-case adenoma sensitivity values were identical for screening and follow-up/surveillance colonoscopies. Ranges of adenoma sensitivity values for colonoscopy performance were developed using different slopes of odds ratio adjustments and were designated as small, medium, or large impact scenarios.

RESULTS

As the differences in adenoma sensitivity for screening versus follow-up/surveillance colonoscopies became greater, life-years gained (LYG) and reductions in CRC-related incidence and mortality versus no screening increased for mt-sDNA and FIT and decreased for screening colonoscopy. The LYG relative to screening colonoscopy reached >90% with FIT in the base-case scenario and with mt-sDNA in a "medium impact" scenario.

CONCLUSIONS

Assuming identical adenoma sensitivities for screening and follow-up/surveillance colonoscopies underestimate the potential benefits of stool-based screening strategies.

Estimating the impact of differential adherence on the comparative effectiveness of stool-based colorectal cancer screening using the CRC-AIM microsimulation model

BACKGROUND

Real-world adherence to colorectal cancer (CRC) screening strategies is imperfect. The CRC-AIM microsimulation model was used to estimate the impact of imperfect adherence on the relative benefits and burdens of guideline-endorsed, stool-based screening strategies.

METHODS

Predicted outcomes of multi-target stool DNA (mt-sDNA), fecal immunochemical tests (FIT), and high-sensitivity guaiac-based fecal occult blood tests (HSgFOBT) were simulated for 40-year-olds free of diagnosed CRC. For robustness, imperfect adherence was incorporated in multiple ways and with extensive sensitivity analysis. Analysis 1 assumed adherence from 0%-100%, in 10% increments. Analysis 2 longitudinally applied real-world first-round differential adherence rates (base-case imperfect rates = 40% annual FIT vs 34% annual HSgFOBT vs 70% triennial mt-sDNA). Analysis 3 randomly assigned individuals to receive 1, 5, or 9 lifetime (9 = 100% adherence) mt-sDNA tests and 1, 5, or 9 to 26 (26 = 100% adherence) FIT tests. Outcomes are reported per 1000 individuals compared with no screening.

RESULTS

Each screening strategy decreased CRC incidence and mortality versus no screening. In individuals screened between ages 50-75 and adherence ranging from 10%a-100%, the life-years gained (LYG) for triennial mt-sDNA ranged from 133.1-300.0, for annual FIT from 96.3-318.1, and for annual HSgFOBT from 99.8-320.6. At base-case imperfect adherence rates, mt-sDNA resulted in 19.1% more LYG versus FIT, 25.4% more LYG versus HSgFOBT, and generally had preferable efficiency ratios while offering the most LYG. Completion of at least 21 FIT tests is needed to reach approximately the same LYG achieved with 9 mt-sDNA tests.

CONCLUSIONS

Adherence assumptions affect the conclusions of CRC screening microsimulations that are used to inform CRC screening guidelines. LYG from FIT and HSgFOBT are more sensitive to changes in adherence assumptions than mt-sDNA because they require more tests be completed for equivalent benefit. At imperfect adherence rates, mt-sDNA provides more LYG than FIT or HSgFOBT at an acceptable tradeoff in screening burden.

Validation of Microsimulation Models against Alternative Model Predictions and Long-Term Colorectal Cancer Incidence and Mortality Outcomes of Randomized Controlled Trials

Background. This study aimed to assess the validity of 2 microsimulation models of colorectal cancer (CRC), Policy1-Bowel and ASCCA. Methods. The model-estimated CRC risk in population subgroups with different health statuses, "dwell time" (time from incident precancerous polyp to symptomatically detected CRC), and reduction in symptomatically detected CRC incidence after a one-time complete removal of polyps and/or undetected CRC were compared with published findings from 3 well-established models (MISCAN, CRC-SPIN, and SimCRC). Furthermore, 6 randomized controlled trials (RCTs) that provided screening using a guaiac fecal occult blood test (Funen trial, Burgundy trial, and Minnesota Colon Cancer Control Study [MCCCS]) or flexible sigmoidoscopy (NORCCAP, SCORE, and UKFSST) with long-term follow-up were simulated. Model-estimated long-term relative reductions of CRC incidence (RR_inc) and mortality (RR_mort) were compared with the RCTs' findings. Results. The Policy1-Bowel and ASCCA estimates showed more similarities to CRC-SPIN and SimCRC. For example, overall dwell times estimated by Policy1-Bowel (24.0 years) and ASCCA (25.3) were comparable to CRC-SPIN (25.8) and SimCRC (25.2) but higher than MISCAN (10.6). In addition, ∼86% of Policy1-Bowel's and ∼74% of ASCCA's estimated RR_inc and RR_mort were consistent with the RCTs' long-term follow-up findings. For example, at 17 to 18 years of follow-up, the MCCCS reported RR_mort of 0.67 (95% confidence interval [CI], 0.51-0.83) and 0.79 (95% CI, 0.62-0.97) for the annual and biennial screening arm, respectively, and the UKFSST reported RR_mort of 0.70 (95% CI, 0.62-0.79) for CRC at all sites and 0.54 (95% CI, 0.46-0.65) for distal CRC. The corresponding model estimates were 0.65, 0.74, 0.81, and 0.61, respectively, for Policy1-Bowel and 0.65, 0.70, 0.75, and 0.58, respectively, for ASCCA. Conclusion. Policy1-Bowel and ASCCA's estimates are largely consistent with the data included for comparisons, which indicates good model validity.

MICROSIMULATION MODEL CALIBRATION USING INCREMENTAL MIXTURE APPROXIMATE BAYESIAN COMPUTATION

Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration, which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Microsimulation Modeling in Oncology

PURPOSE

Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine.

METHODS

We conducted a comprehensive and methodical search of the literature using electronic databases-Medline, Embase, and Cochrane-for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as "microsimulation model medicine," "multistate modeling cancer," and "oncology."

RESULTS

Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population.

CONCLUSION

A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.

External Validation of a Colorectal Cancer Model Against Screening Trial Long-Term Follow-Up Data

BACKGROUND

The University of Sheffield School of Health and Related Research (ScHARR) Bowel Cancer Screening Model has been used previously to make decisions about colorectal cancer screening strategies in England.

OBJECTIVES

The objective of this study was to perform an external validation of the ScHARR model against long-term follow-up data about colorectal cancer (CRC) incidence and mortality reductions due to screening, from the Nottingham trial of guaiac faecal occult blood testing for CRC, and the UK Flexible Sigmoidoscopy Screening Trial.

METHODS

The ScHARR model was adapted prior to validation to reflect the setting of each trial in terms of population characteristics, details of screening and surveillance programs, uptake of screening, and further investigations and study follow-up. The impact of using current versus historical CRC incidence and mortality data in the validation was also examined by carrying out a series of analyses in which historical data from different years was included in the model.

RESULTS

The ScHARR model was able to predict CRC incidence and mortality rate/hazard ratios from both trials to well within the 95% confidence intervals in the observed data. While it was less accurate in predicting absolute incidence and mortality rates, modeling historical incidence and mortality data enabled these predictions to be improved considerably.

CONCLUSION

The ScHARR model is able to replicate the long-term relative benefit from screening observed in 2 large-scale UK-based screening trials and can therefore be considered to be an appropriate tool to facilitate decision making around the English bowel cancer screening program.

Effect of Time to Diagnostic Testing for Breast, Cervical, and Colorectal Cancer Screening Abnormalities on Screening Efficacy: A Modeling Study

Background: Patients who receive an abnormal cancer screening result require follow-up for diagnostic testing, but the time to follow-up varies across patients and practices.Methods: We used a simulation study to estimate the change in lifetime screening benefits when time to follow-up for breast, cervical, and colorectal cancers was increased. Estimates were based on four independently developed microsimulation models that each simulated the life course of adults eligible for breast (women ages 50-74 years), cervical (women ages 21-65 years), or colorectal (adults ages 50-75 years) cancer screening. We assumed screening based on biennial mammography for breast cancer, triennial Papanicolaou testing for cervical cancer, and annual fecal immunochemical testing for colorectal cancer. For each cancer type, we simulated diagnostic testing immediately and at 3, 6, and 12 months after an abnormal screening exam.Results: We found declines in screening benefit with longer times to diagnostic testing, particularly for breast cancer screening. Compared to immediate diagnostic testing, testing at 3 months resulted in reduced screening benefit, with fewer undiscounted life years gained per 1,000 screened (breast: 17.3%, cervical: 0.8%, colorectal: 2.0% and 2.7%, from two colorectal cancer models), fewer cancers prevented (cervical: 1.4% fewer, colorectal: 0.5% and 1.7% fewer, respectively), and, for breast and colorectal cancer, a less favorable stage distribution.Conclusions: Longer times to diagnostic testing after an abnormal screening test can decrease screening effectiveness, but the impact varies substantially by cancer type.Impact: Understanding the impact of time to diagnostic testing on screening effectiveness can help inform quality improvement efforts. Cancer Epidemiol Biomarkers Prev; 27(2); 158-64. ©2017 AACR.

CMOST: an open-source framework for the microsimulation of colorectal cancer screening strategies

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related mortality. CRC incidence and mortality can be reduced by several screening strategies, including colonoscopy, but randomized CRC prevention trials face significant obstacles such as the need for large study populations with long follow-up. Therefore, CRC screening strategies will likely be designed and optimized based on computer simulations. Several computational microsimulation tools have been reported for estimating efficiency and cost-effectiveness of CRC prevention. However, none of these tools is publicly available. There is a need for an open source framework to answer practical questions including testing of new screening interventions and adapting findings to local conditions.

METHODS

We developed and implemented a new microsimulation model, Colon Modeling Open Source Tool (CMOST), for modeling the natural history of CRC, simulating the effects of CRC screening interventions, and calculating the resulting costs. CMOST facilitates automated parameter calibration against epidemiological adenoma prevalence and CRC incidence data.

RESULTS

Predictions of CMOST were highly similar compared to a large endoscopic CRC prevention study as well as predictions of existing microsimulation models. We applied CMOST to calculate the optimal timing of a screening colonoscopy. CRC incidence and mortality are reduced most efficiently by a colonoscopy between the ages of 56 and 59; while discounted life years gained (LYG) is maximal at 49-50 years. With a dwell time of 13 years, the most cost-effective screening is at 59 years, at $17,211 discounted USD per LYG. While cost-efficiency varied according to dwell time it did not influence the optimal time point of screening interventions within the tested range.

CONCLUSIONS

Predictions of CMOST are highly similar compared to a randomized CRC prevention trial as well as those of other microsimulation tools. This open source tool will enable health-economics analyses in for various countries, health-care scenarios and CRC prevention strategies. CMOST is freely available under the GNU General Public License at https://gitlab.com/misselwb/CMOST.

Bayesian Methods for Calibrating Health Policy Models: A Tutorial

Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes' theorem. This article provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique theoretically optimal summary of the evidence.

Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves

BACKGROUND

Despite some advances, cardiovascular disease (CVD) remains the leading cause of death and healthcare costs in the United States. We therefore developed a comprehensive CVD policy simulation model that identifies cost-effective approaches for reducing CVD burden. This paper aims to: 1) describe our model in detail; and 2) perform model validation analyses.

METHODS

The model simulates 1,000,000 adults (ages 35 to 80 years) using a variety of CVD-related epidemiological data, including previously calibrated Framingham-based risk scores for coronary heart disease and stroke. We validated our microsimulation model using recent National Health and Nutrition Examination Survey (NHANES) data, with baseline values collected in 1999-2000 and cause-specific mortality follow-up through 2011. Model-based (simulated) results were compared to observed all-cause and CVD-specific mortality data (from NHANES) for the same starting population using survival curves and, in a method not typically used for disease model validation, receiver operating characteristic (ROC) curves.

RESULTS

Observed 10-year all-cause mortality in NHANES v. the simulation model was 11.2% (95% CI, 10.3% to 12.2%) v. 10.9%; corresponding results for CVD mortality were 2.2% (1.8% to 2.7%) v. 2.6%. Areas under the ROC curves for model-predicted 10-year all-cause and CVD mortality risks were 0.83 (0.81 to 0.85) and 0.84 (0.81 to 0.88), respectively; corresponding results for 5-year risks were 0.80 (0.77 to 0.83) and 0.81 (0.75 to 0.87), respectively.

LIMITATIONS

The model is limited by the uncertainties in the data used to estimate its input parameters. Additionally, our validation analyses did not include non-fatal CVD outcomes due to NHANES data limitations.

CONCLUSIONS

The simulation model performed well in matching to observed nationally representative longitudinal mortality data. ROC curve analysis, which has been traditionally used for risk prediction models, can also be used to assess discrimination for disease simulation models.

Estimating the Effect of Targeted Screening Strategies: An Application to Colonoscopy and Colorectal Cancer

Supplemental Digital Content is available in the text.

Screening behavior depends on previous screening history and family members’ behaviors, which can act as both confounders and intermediate variables on a causal pathway from screening to disease risk. Conventional analyses that adjust for these variables can lead to incorrect inferences about the causal effect of screening if high-risk individuals are more likely to be screened. Analyzing the data in a manner that treats screening as randomized conditional on covariates allows causal parameters to be estimated; inverse probability weighting based on propensity of exposure scores is one such method considered here. I simulated family data under plausible models for the underlying disease process and for screening behavior to assess the performance of alternative methods of analysis and whether a targeted screening approach based on individuals’ risk factors would lead to a greater reduction in cancer incidence in the population than a uniform screening policy. Simulation results indicate that there can be a substantial underestimation of the effect of screening on subsequent cancer risk when using conventional analysis approaches, which is avoided by using inverse probability weighting. A large case–control study of colonoscopy and colorectal cancer from Germany shows a strong protective effect of screening, but inverse probability weighting makes this effect even stronger. Targeted screening approaches based on either fixed risk factors or family history yield somewhat greater reductions in cancer incidence with fewer screens needed to prevent one cancer than population-wide approaches, but the differences may not be large enough to justify the additional effort required. See video abstract at, http://links.lww.com/EDE/B207.

Understanding the Effects of Competition for Constrained Colonoscopy Services with the Introduction of Population-level Colorectal Cancer Screening

BACKGROUND

Median wait times for gastroenterology services in Canada exceed consensus-recommended targets and have worsened substantially over the past decade. Meanwhile, efforts to control colorectal cancer have shifted their focus to screening asymptomatic, average-risk individuals. Along with increasing prevalence of colorectal cancer due to an aging population, screening programs are expected to add substantially to the existing burden on colonoscopy services, and create competition for limited services among individuals of varying risk. Failure to understand the effects of operational programmatic screening decisions may cause unintended harm to both screening participants and higher-risk patients, make inefficient use of limited health care resources, and ultimately hinder a program's success.

METHODS

We present a new simulation model (Simulation of Cancer Outcomes for Planning Exercises, or SCOPE) for colorectal cancer screening which, unlike many other colorectal cancer screening models, reflects the effects of competition for limited colonoscopy services between patient groups and can be used to guide planning to ensure adequate resource allocation. We include verification and validation results for the SCOPE model.

RESULTS

A discrete event simulation model was developed based on an epidemiological representation of colorectal cancer in a sample population. Colonoscopy service and screening modules were added to allow observation of screening scenarios and resource considerations. The model reproduces population-based data on prevalence of colorectal cancer by stage, and mortality by cause of death, age, and sex, and attendant demand and wait times for colonoscopy services.

CONCLUSIONS

The study model differs from existing screening models in that it explicitly considers the colonoscopy resource implications of screening activities and the impact of constrained resources on screening effectiveness.

Validation of Models Used to Inform Colorectal Cancer Screening Guidelines: Accuracy and Implications

BACKGROUND

Microsimulation models synthesize evidence about disease processes and interventions, providing a method for predicting long-term benefits and harms of prevention, screening, and treatment strategies. Because models often require assumptions about unobservable processes, assessing a model's predictive accuracy is important.

METHODS

We validated 3 colorectal cancer (CRC) microsimulation models against outcomes from the United Kingdom Flexible Sigmoidoscopy Screening (UKFSS) Trial, a randomized controlled trial that examined the effectiveness of one-time flexible sigmoidoscopy screening to reduce CRC mortality. The models incorporate different assumptions about the time from adenoma initiation to development of preclinical and symptomatic CRC. Analyses compare model predictions to study estimates across a range of outcomes to provide insight into the accuracy of model assumptions.

RESULTS

All 3 models accurately predicted the relative reduction in CRC mortality 10 years after screening (predicted hazard ratios, with 95% percentile intervals: 0.56 [0.44, 0.71], 0.63 [0.51, 0.75], 0.68 [0.53, 0.83]; estimated with 95% confidence interval: 0.56 [0.45, 0.69]). Two models with longer average preclinical duration accurately predicted the relative reduction in 10-year CRC incidence. Two models with longer mean sojourn time accurately predicted the number of screen-detected cancers. All 3 models predicted too many proximal adenomas among patients referred to colonoscopy.

CONCLUSION

Model accuracy can only be established through external validation. Analyses such as these are therefore essential for any decision model. Results supported the assumptions that the average time from adenoma initiation to development of preclinical cancer is long (up to 25 years), and mean sojourn time is close to 4 years, suggesting the window for early detection and intervention by screening is relatively long. Variation in dwell time remains uncertain and could have important clinical and policy implications.

Cancer Models and Real-world Data: Better Together

Decision-analytic models are increasingly used to inform health policy decisions. These models synthesize available data on disease burden and intervention effectiveness to project estimates of the long-term consequences of care, which are often absent when clinical or policy decisions must be made. While models have been influential in informing US cancer screening guidelines under ideal conditions, incorporating detailed data on real-world screening practice has been limited given the complexity of screening processes and behaviors throughout diverse health delivery systems in the United States. We describe the synergies that exist between decision-analytic models and health care utilization data that are increasingly accessible through research networks that assemble data from the growing number of electronic medical record systems. In particular, we present opportunities to enrich cancer screening models by grounding analyses in real-world data with the goals of projecting the harms and benefits of current screening practices, evaluating the value of existing and new technologies, and identifying the weakest links in the cancer screening process where efforts for improvement may be most productively focused. We highlight the example of the National Cancer Institute-funded consortium Population-based Research Optimizing Screening through Personalized Regimens (PROSPR), a collaboration to harmonize and analyze screening process and outcomes data on breast, colorectal, and cervical cancers across seven research centers. The pairing of models with such data can create more robust models to not only better inform policy but also inform health care systems about best approaches to improve the provision of cancer screening in the United States.

Personalizing age of cancer screening cessation based on comorbid conditions: model estimates of harms and benefits

BACKGROUND

Harms and benefits of cancer screening depend on age and comorbid conditions, but reliable estimates are lacking.

OBJECTIVE

To estimate the harms and benefits of cancer screening by age and comorbid conditions to inform decisions about screening cessation.

DESIGN

Collaborative modeling with 7 cancer simulation models and common data on average and comorbid condition level-specific life expectancy.

SETTING

U.S. population.

PATIENTS

U.S. cohorts aged 66 to 90 years in 2010 with average health or 1 of 4 comorbid condition levels: none, mild, moderate, or severe.

INTERVENTION

Mammography, prostate-specific antigen testing, or fecal immunochemical testing.

MEASUREMENTS

Lifetime cancer deaths prevented and life-years gained (benefits); false-positive test results and overdiagnosed cancer cases (harms). For each comorbid condition level, the age at which harms and benefits of screening were similar to that for persons with average health having screening at age 74 years.

RESULTS

Screening 1000 women with average life expectancy at age 74 years for breast cancer resulted in 79 to 96 (range across models) false-positive results, 0.5 to 0.8 overdiagnosed cancer cases, and 0.7 to 0.9 prevented cancer deaths. Although absolute numbers of harms and benefits differed across cancer sites, the ages at which to cease screening were consistent across models and cancer sites. For persons with no, mild, moderate, and severe comorbid conditions, screening until ages 76, 74, 72, and 66 years, respectively, resulted in harms and benefits similar to average-health persons.

LIMITATION

Comorbid conditions influenced only life expectancy.

CONCLUSION

Comorbid conditions are an important determinant of harms and benefits of screening. Estimates of screening benefits and harms by comorbid condition can inform discussions between providers and patients about personalizing screening cessation decisions.

PRIMARY FUNDING SOURCE

National Cancer Institute and Centers for Disease Control and Prevention.

Modeling the Adenoma and Serrated pathway to Colorectal CAncer (ASCCA)

Several colorectal cancer (CRC) screening models have been developed describing the progression of adenomas to CRC. Currently, there is increasing evidence that serrated lesions can also develop into CRC. It is not clear whether screening tests have the same test characteristics for serrated lesions as for adenomas, but lower sensitivities have been suggested. Models that ignore this type of colorectal lesions may provide overly optimistic predictions of the screen-induced reduction in CRC incidence. To address this issue, we have developed the Adenoma and Serrated pathway to Colorectal CAncer (ASCCA) model that includes the adenoma-carcinoma pathway and the serrated pathway to CRC as well as characteristics of colorectal lesions. The model structure and the calibration procedure are described in detail. Calibration resulted in 19 parameter sets for the adenoma-carcinoma pathway and 13 for the serrated pathway that match the age- and sex-specific adenoma and serrated lesion prevalence in the COlonoscopy versus COlonography Screening (COCOS) trial, Dutch CRC incidence and mortality rates, and a number of other intermediate outcomes concerning characteristics of colorectal lesions. As an example, we simulated outcomes for a biennial fecal immunochemical test screening program and a hypothetical one-time colonoscopy screening program. Inclusion of the serrated pathway influenced the predicted effectiveness of screening when serrated lesions are associated with lower screening test sensitivity or when they are not removed. To our knowledge, this is the first model that explicitly includes the serrated pathway and characteristics of colorectal lesions. It is suitable for the evaluation of the (cost)effectiveness of potential screening strategies for CRC.

Evaluating risk factor assumptions: a simulation-based approach

BACKGROUND

Microsimulation models are an important tool for estimating the comparative effectiveness of interventions through prediction of individual-level disease outcomes for a hypothetical population. To estimate the effectiveness of interventions targeted toward high risk groups, the mechanism by which risk factors influence the natural history of disease must be specified. We propose a method for evaluating these risk factor assumptions as part of model-building.

METHODS

We used simulation studies to examine the impact of risk factor assumptions on the relative rate (RR) of colorectal cancer (CRC) incidence and mortality for a cohort with a risk factor compared to a cohort without the risk factor using an extension of the CRC-SPIN model for colorectal cancer. We also compared the impact of changing age at initiation of screening colonoscopy for different risk mechanisms.

RESULTS

Across CRC-specific risk factor mechanisms, the RR of CRC incidence and mortality decreased (towards one) with increasing age. The rate of change in RRs across age groups depended on both the risk factor mechanism and the strength of the risk factor effect. Increased non-CRC mortality attenuated the effect of CRC-specific risk factors on the RR of CRC when both were present. For each risk factor mechanism, earlier initiation of screening resulted in more life years gained, though the magnitude of life years gained varied across risk mechanisms.

CONCLUSIONS

Simulation studies can provide insight into both the effect of risk factor assumptions on model predictions and the type of data needed to calibrate risk factor models.

Computer disease simulation models: integrating evidence for health policy

Computer disease simulation models are increasingly being used to evaluate and inform health care decisions across medical disciplines. The aim of researchers who develop these models is to integrate and synthesize short-term outcomes and results from multiple sources to predict the long-term clinical outcomes and costs of different health care strategies. Policy makers, in turn, can use the predictions generated by disease models together with other evidence to make decisions related to health care practices and resource utilization. Models are particularly useful when the existing evidence does not yield obvious answers or does not provide answers to the questions of greatest interest, such as questions about the relative cost-effectiveness of different practices. This review focuses on models used to inform decisions about imaging technology, discussing the role of disease models for health policy development and providing a foundation for understanding the basic principles of disease modeling. This manuscript draws from the collective computed tomographic colonography modeling experience, reviewing 10 published investigations of the clinical effectiveness and cost-effectiveness of computed tomographic colonography relative to colonoscopy. The discussion focuses on implications of different modeling assumptions and difficulties that may be encountered when evaluating the quality of models. This underscores the importance of forging stronger collaborations between researchers who develop disease models and radiologists, to ensure that policy-level models accurately represent the experience of everyday clinical practices.

Clarifying differences in natural history between models of screening: the case of colorectal cancer

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.