All Times ET
Keywords: Model Calibration, Simulation models, Latin Hypercube sampling, cohort-based models, OUD shared decision making
Simulation models of opioid use disorder (OUD) aim at evaluating the impact of different treatment strategies on population-level outcomes. Researching Effective Strategies to Prevent Opioid Death (RESPOND) is a cohort-based model that simulates the Massachusetts (MA) OUD population synthesizing data from the MA Public Health Data Warehouse, published survey studies and randomized trials. We implement an empirical calibration approach to fit RESPOND to multiple calibration targets, including yearly counts of fatal overdoses and detox admissions in 2013-2015, and 2015 OUD population counts in MA. We used capture-recapture analysis to estimate the OUD population, and to quantify uncertainty around calibration targets (Barocas et al. AJPH 2018). The empirical calibration approach involves Latin Hypercube Sampling for an efficient search of the multidimensional parameter space, comprising demographics of “arrivals”, overdose rates, treatment transition probabilities and substance use transition probabilities. The algorithm accepts proposed parameter values when the respective model outputs are “close” to the observed calibration targets based on pre-specified Goodness of Fit measures. Preliminary runs have shown a good fit of the model to calibration targets OUD population and fatal overdose counts. The flexibility of the algorithm also allows us to identify certain "questionable" parts of the model structure and explore the underlying relationships between the model parameters in an efficient manner. The calibrated model is also externally validated by comparing other model outcomes to observed data: all-cause mortality, active OUD counts, treatment admissions and overdose rates. In addition, the resulting set of values for the calibrated parameters will inform the priors of a more comprehensive Bayesian calibration. The calibrated RESPOND model will be employed to improve shared decision making for OUD.