Model parameters.

Background

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 dynamic population, state-transition model that simulates the Massachusetts OUD population synthesizing data from multiple sources. Structural complexity and scarcity of available data for opioid modeling pose a special challenge to model calibration. We propose an empirical calibration approach applicable to complex simulation models in general.

Methods

We implement an empirical approach to calibrate RESPOND to multiple targets: annual fatal opioid-related overdoses, detox admissions, and OUD population sizes. The empirical calibration involves Latin hypercube sampling for searching a multidimensional parameter space comprising arrivals, overdose rates, treatment transition rates, and substance use state transition probabilities. The algorithm accepts proposed parameters when the respective model outputs lie within pre-determined target uncertainty ranges. This is an iterative process resulting in a set of parameter values for which the model closely fits all the calibration targets. We validated the model assessing its accuracy to projections important for shared decision-making of OUD outside the training data.

Results

The empirical calibration resulted in a model that fits well both calibration and validation targets. The flexibility of the algorithm allowed us to explore structural and parameter uncertainty, reveal underlying relationships between model parameters and identify areas of model improvement for a more accurate representation of the OUD dynamics.

Discussion

The proposed empirical calibration approach is an efficient tool for approximating parameter distributions of complex models, especially under complete uncertainty. Empirically calibrated parameters can be used as a starting point for a more comprehensive calibration exercise, e.g., to inform priors of a Bayesian calibration. The calibrated RESPOND model can be used to improve shared decision-making for OUD.

研究背景

阿片类使用障碍（opioid use disorder, OUD）的模拟模型旨在评估不同治疗策略对人群层面结局的影响。有效预防阿片类死亡研究计划（Researching Effective Strategies to Prevent Opioid Death, RESPOND）是一项动态人群状态转换模型（state-transition model），该模型通过整合多源数据，对马萨诸塞州的阿片类使用障碍人群开展模拟。阿片类建模所面临的结构复杂性与可用数据稀缺性，给模型校准带来了特殊挑战。我们提出了一种经验校准方法，该方法可普遍适用于各类复杂模拟模型。

研究方法

我们采用经验校准方法，将RESPOND模型适配至三类校准目标：年度致命阿片类相关过量用药事件、戒毒收治人数以及阿片类使用障碍人群规模。该经验校准过程采用拉丁超立方抽样（Latin hypercube sampling），对包含人群流入率、过量用药率、治疗转换率以及物质使用状态转换概率在内的多维参数空间进行搜索。当模型对应输出结果落在预先设定的目标不确定性区间内时，算法将接受该组候选参数。这是一个迭代优化过程，最终可得到一组使模型与所有校准目标高度契合的参数值。我们通过验证模型对训练数据之外、与阿片类使用障碍共同决策相关的预测结果的准确性，完成了模型验证。

研究结果

经经验校准得到的模型，能够较好地适配校准与验证两类目标。该算法具备良好的灵活性，使得我们可以探究结构与参数不确定性，揭示模型参数间的内在关联，并识别模型改进方向，从而更精准地刻画阿片类使用障碍的动态变化过程。

讨论

本研究提出的经验校准方法，是一种高效工具，可用于拟合复杂模型的参数分布，尤其适用于存在完全不确定性的场景。经经验校准得到的参数，可作为更全面校准工作的起点，例如为贝叶斯校准（Bayesian calibration）提供先验信息。经校准后的RESPOND模型，可用于优化阿片类使用障碍的共同决策流程。