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Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters' posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold. Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

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BACKGROUND: Clinical trials often report intervention efficacy in terms of the reduction in all-cause mortality between the treatment and control arms (i.e., an overall hazard ratio [oHR]) instead of the reduction in disease-specific mortality (i.e., a disease-specific hazard ratio [dsHR]). Using oHR to reduce all-cause mortality beyond the time horizon of the trial may introduce bias if the relative proportion of other-cause mortality increases with age. We sought to quantify this oHR extrapolation bias and propose a new approach to overcome this bias. METHODS: We simulated a hypothetical cohort of patients with a generic disease that increased background mortality by a constant additive disease-specific rate. We quantified the bias in terms of the percentage change in life expectancy gains with the intervention under an oHR compared with a dsHR approach as a function of the cohort start age, the disease-specific mortality rate, dsHR, and the duration of the intervention's effect. We then quantified the bias in a cost-effectiveness analysis (CEA) of implantable cardioverter-defibrillators based on efficacy estimates from a clinical trial. RESULTS: For a cohort of 50-year-old patients with a disease-specific mortality of 0.05, a dsHR of 0.5, a calculated oHR of 0.55, and a lifetime duration of effect, the bias was 28%. We varied these key parameters over wide ranges and the resulting bias ranged between 3 and 140%. In the CEA, the use of oHR as the intervention's effectiveness overestimated quality-adjusted life expectancy by 9% and costs by 3%, biasing the incremental cost-effectiveness ratio by - 6%. CONCLUSIONS: The use of an oHR approach to model the intervention's effectiveness beyond the time horizon of the trial overestimates its benefits. In CEAs, this bias could decrease the cost of a QALY, overestimating interventions' cost effectiveness.

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Although epidemiologic studies have established the relationship between Helicobacter pylori and gastric cancer and promising results that H. pylori treatment can reduce cancer incidence among individuals without preexisting precancerous lesions, there is no consensus on whether screening for H. pylori should be conducted. Our objective was to synthesize the available data to develop and empirically calibrate a mathematical model of gastric cancer and H. pylori in China and Colombia that could be used to provide qualitative insight into the benefits and cost-effectiveness of primary and secondary gastric cancer prevention strategies. The model represents the natural history of noncardia intestinal type gastric adenocarcinomas as a sequence of transitions among health states (e.g., normal gastric mucosa, chronic nonatrophic gastritis, gastric atrophy, intestinal metaplasia, dysplasia, and gastric cancer) stratified by H. pylori status. Initial plausible ranges for each parameter were established using data from published literature. A likelihood-based empirical calibration approach was used to identify multiple good-fitting parameter sets that were consistent with epidemiologic data. We then used these parameter sets to estimate a range of likely outcomes associated with H. pylori screening. This modeling approach allows for parameter uncertainty surrounding the natural history of H. pylori and gastric cancer to be reflected in the results of comparative analyses of different gastric cancer prevention strategies. As better data become available, the model can be refined and recalibrated, and, as such, be used as an iterative tool to assess the likely health and economic outcomes associated with gastric cancer prevention strategies.

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The objective of this study was to develop a comprehensive natural history model of human papillomavirus (HPV) and cervical cancer using a two-step approach to model calibration. In the first step, the authors utilized primary epidemiologic data from a longitudinal study of women in Brazil and identified a plausible range for each input parameter that produced model output within the 95% confidence intervals of the data. In the second step, they performed a simultaneous search over all input parameters to identify parameter sets that produced output consistent with data from multiple sources. A goodness-of-fit score was computed for 555,000 unique parameter sets using a likelihood-based approach, and a sample of good-fitting parameter sets was used in the model to illustrate the advantage of the calibration approach by projecting a range of benefits associated with cervical cancer prevention policies. The calibrated model had reasonable fit to the data in terms of duration and prevalence of HPV infection for high-risk types, prevalence of precancerous lesions, and incidence of cancer. The authors found that leveraging primary data from longitudinal studies provides unique opportunities for model parameterization of the unobservable nature of HPV infection and its role in the development of cervical cancer.

Assuntos