Bayesian Methods for Calibrating Health Policy Models: A Tutorial

Menzies, Nicolas A; Soeteman, Djøra I.; Pandya, Ankur; Kim, Jane J.

doi:10.1007/s40273-017-0494-4

Cited by 58 publications

(80 citation statements)

References 38 publications

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“…The values of many parameters in tuberculosis and HIV co-epidemics models are not known with certainty. Therefore, we adopted a Bayesian calibration approach 27 to identify parameter sets that resulted in simulated trajectories with good fit to available epidemiological data ( table 2 ). To implement this approach, we specified previous distributions for each parameter.…”

Section: Methodsmentioning

confidence: 99%

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

Marx

Yaesoubi²,

Menzies

et al. 2018

The Lancet Global Health

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Summary Background In high-incidence settings, recurrent disease among previously treated individuals contributes substantially to the burden of incident and prevalent tuberculosis. The extent to which interventions targeted to this high-risk group can improve tuberculosis control has not been established. We aimed to project the population-level effect of control interventions targeted to individuals with a history of previous tuberculosis treatment in a high-incidence setting. Methods We developed a transmission-dynamic model of tuberculosis and HIV in a high-incidence setting with a population of roughly 40 000 people in suburban Cape Town, South Africa. The model was calibrated to data describing local demography, TB and HIV prevalence, TB case notifications and treatment outcomes using a Bayesian calibration approach. We projected the effect of annual targeted active case finding in all individuals who had previously completed tuberculosis treatment and targeted active case finding combined with lifelong secondary isoniazid preventive therapy. We estimated the effect of these targeted interventions on local tuberculosis incidence, prevalence, and mortality over a 10 year period (2016–25). Findings We projected that, under current control efforts in this setting, the tuberculosis epidemic will remain in slow decline for at least the next decade. Additional interventions targeted to previously treated people could greatly accelerate these declines. We projected that annual targeted active case finding combined with secondary isoniazid preventive therapy in those who previously completed tuberculosis treatment would avert 40% (95% uncertainty interval [UI] 21–56) of incident tuberculosis cases and 41% (16–55) of tuberculosis deaths occurring between 2016 and 2025. Interpretation In this high-incidence setting, the use of targeted active case finding in combination with secondary isoniazid preventive therapy in previously treated individuals could accelerate decreases in tuberculosis morbidity and mortality. Studies to measure cost and resource implications are needed to establish the feasibility of this type of targeted approach for improving tuberculosis control in settings with high tuberculosis and HIV prevalence. Funding National Institutes of Health, German Research Foundation.

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Section: Methodsmentioning

confidence: 99%

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

Marx

Yaesoubi²,

Menzies

et al. 2018

The Lancet Global Health

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“…Individuals who developed lung cancers may (a) progress to any higher stage, (b) remain in their current stage, (c) get diagnosed or (d) die within the current cycle. Transition probabilities between lung cancer states and incidence rates were estimated using Bayesian calibration methods and German incidence data from the German Centre for Cancer Registry Data [37][38][39]. We set up a Metropolis Hastings algorithm with 50,000 runs and a "burn in" of 10,000 runs [33,40].…”

Section: Model Structurementioning

confidence: 99%

Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach

Hofer¹,

Kauczor

Stargardt³

2018

Lung Cancer

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Background: Lung cancer is the leading cause of cancer death in Germany. Although several randomized trials in Europe have evaluated the effectiveness of lung cancer screening programs, evidence on the cost-effectiveness of lung cancer screening is scarce. Objective: To evaluate the cost-effectiveness of a population-based lung cancer screening program from the perspective of a German payer. Methods: We conducted a cost-effectiveness analysis from the public payer perspective for a high-risk population defined as heavy former and current smokers (≥20 cigarettes per day) between 55 and 75 years of age. The underlying model consisted of two Markov models. We differentiated between a population-based annual screening program and standard clinical care. Depending on stage at diagnosis, simulated patients were assigned to one of five treatment paths according to the German clinical guideline for the diagnosis and treatment of lung cancer. Costs, life years saved, and quality adjusted life years (QALYs) were used as outcomes. Values for input parameters were taken from the literature. The model was run for 60 cycles with a cycle length of three months. Deterministic and probabilistic sensitivity analyses were conducted. Results: In the base case, annual lung cancer screening led to an increase in incremental costs (€ 1,153 per person) compared to standard clinical care. However, the screening approach was associated with an incremental gain in life years (0.06 per person) and QALYs (0.04 per person). Thus, the incremental cost-effectiveness ratio (ICER) was € 19,302 per life year saved and € 30,291 per QALY. A probabilistic sensitivity analysis with 10,000 draws resulted in average ICERs of € 22,118 per life year and € 34,841 per QALY. Conclusion: We provide evidence that lung cancer screening for a high-risk population may be more effective, but also more costly, than standard clinical care from the perspective of a German payer.

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“…Given that many parameters of EPIC are derived based on calibration techniques, this is not simply a matter of replacing fixed input parameters with their probabilistic equivalent. More sophisticated model calibration techniques might prove useful in this context(48). Further, EPIC is currently limited to modeling the impact of sex, age, and smoking as risk factors for COPD.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of the Evaluation Platform In COPD (EPIC): a population-based outcomes model of COPD for Canada

Sadatsafavi

Ghanbarian

Adibi

et al. 2018

Preprint

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BackgroundWe report the development, validation, and implementation of an open-source population-based outcomes model of Chronic Obstructive Pulmonary Disease (COPD) for Canada.MethodsEvaluation Platform in COPD (EPIC) is a discrete event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity as well as internal and external validity evaluations were performed.ResultsThe model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated rate of overall exacerbations in two independent trials, but generated consistent estimates of rate of severe exacerbations and mortality.LimitationsIn its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations.ConclusionsEPIC is the first multi-purpose outcome- and policy-focused model of COPD for Canada. By modeling the natural history of COPD from its inception, it is capable of modeling the outcomes of decisions across the entire care pathway of COPD. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

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Bayesian Methods for Calibrating Health Policy Models: A Tutorial

Cited by 58 publications

References 38 publications

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

Tuberculosis control interventions targeted to previously treated people in a high-incidence setting: a modelling study

Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach

Development and validation of the Evaluation Platform In COPD (EPIC): a population-based outcomes model of COPD for Canada

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