PharmacoEconomics · 2026
Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Adair Olivia, McFerran Ethna, Lawler Mark, van Duuren Luuk A, Lamrock Felicity
Аннотация
BACKGROUND: Individual-level microsimulation models are essential for evaluating colorectal cancer (CRC) screening programmes to capture the heterogeneity in disease progression. To ensure regional relevance, such models require detailed natural history structures and robust calibration to population-specific data. This study presents the development of the first CRC natural history microsimulation model tailored to Northern Ireland (NI) for evaluating the NI Bowel Cancer Screening Programme (NI BCSP).
METHOD: The model simulates individual trajectories from adenoma onset to CRC diagnosis. Eight natural history parameters were calibrated to sex-specific CRC incidence data, initially using empirical (frequentist) calibration and Approximate Bayesian Computation (ABC) rejection, followed by the ABC-Markov Chain Monte Carlo (ABC-MCMC) algorithm. Other parameters were informed by NI-specific data sources.
RESULTS: The frequentist and ABC rejection calibration approach's posterior distributions informed the prior distribution for the ABC-MCMC approach. ABC-MCMC was informative, yielding 55 parameter sets, but results were constrained by limited calibration targets and parameter identifiability.
CONCLUSION: This is the first NI-specific CRC microsimulation model, providing a regionally tailored platform for evaluating screening strategies and supporting policy. Calibration was feasible in a data-limited context, but further refinement and additional targets are needed to improve parameter estimation.
METHOD: The model simulates individual trajectories from adenoma onset to CRC diagnosis. Eight natural history parameters were calibrated to sex-specific CRC incidence data, initially using empirical (frequentist) calibration and Approximate Bayesian Computation (ABC) rejection, followed by the ABC-Markov Chain Monte Carlo (ABC-MCMC) algorithm. Other parameters were informed by NI-specific data sources.
RESULTS: The frequentist and ABC rejection calibration approach's posterior distributions informed the prior distribution for the ABC-MCMC approach. ABC-MCMC was informative, yielding 55 parameter sets, but results were constrained by limited calibration targets and parameter identifiability.
CONCLUSION: This is the first NI-specific CRC microsimulation model, providing a regionally tailored platform for evaluating screening strategies and supporting policy. Calibration was feasible in a data-limited context, but further refinement and additional targets are needed to improve parameter estimation.