Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models
Fernando Alarid-Escudero, Profesor Investigador Titular de la División de Administración Pública del CIDE, Amy B. Knudsen, Jonathan Ozik, Nicholson Collier y Karen M. Kuntz escribieron el artículo Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models en la revista a Frontiers in Physiology.
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.
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