Karnon Jonathan, Haji Ali Afzali Hossein
School of Population Health, University of Adelaide, Adelaide, Australia,
Pharmacoeconomics. 2014 Jun;32(6):547-58. doi: 10.1007/s40273-014-0147-9.
Modelling in economic evaluation is an unavoidable fact of life. Cohort-based state transition models are most common, though discrete event simulation (DES) is increasingly being used to implement more complex model structures. The benefits of DES relate to the greater flexibility around the implementation and population of complex models, which may provide more accurate or valid estimates of the incremental costs and benefits of alternative health technologies. The costs of DES relate to the time and expertise required to implement and review complex models, when perhaps a simpler model would suffice. The costs are not borne solely by the analyst, but also by reviewers. In particular, modelled economic evaluations are often submitted to support reimbursement decisions for new technologies, for which detailed model reviews are generally undertaken on behalf of the funding body. This paper reports the results from a review of published DES-based economic evaluations. Factors underlying the use of DES were defined, and the characteristics of applied models were considered, to inform options for assessing the potential benefits of DES in relation to each factor. Four broad factors underlying the use of DES were identified: baseline heterogeneity, continuous disease markers, time varying event rates, and the influence of prior events on subsequent event rates. If relevant, individual-level data are available, representation of the four factors is likely to improve model validity, and it is possible to assess the importance of their representation in individual cases. A thorough model performance evaluation is required to overcome the costs of DES from the users' perspective, but few of the reviewed DES models reported such a process. More generally, further direct, empirical comparisons of complex models with simpler models would better inform the benefits of DES to implement more complex models, and the circumstances in which such benefits are most likely.
在经济评估中进行建模是生活中不可避免的事实。基于队列的状态转换模型最为常见，不过离散事件模拟（DES）正越来越多地被用于实现更复杂的模型结构。DES的优势在于在实现和填充复杂模型方面具有更大的灵活性，这可能会为替代健康技术的增量成本和效益提供更准确或有效的估计。DES的成本涉及实现和审查复杂模型所需的时间和专业知识，而此时或许一个更简单的模型就足够了。成本不仅由分析师承担，评审人员也需承担部分成本。特别是，建模的经济评估通常会被提交以支持新技术的报销决策，为此通常会代表资助机构进行详细的模型评审。本文报告了对已发表的基于DES的经济评估进行综述的结果。确定了使用DES的潜在因素，并考虑了应用模型的特征，以便为评估DES在每个因素方面的潜在益处提供参考。确定了使用DES的四个主要因素：基线异质性、连续疾病标志物、随时间变化的事件发生率以及先前事件对后续事件发生率的影响。如果有相关的个体层面数据可用，考虑这四个因素可能会提高模型的有效性，并且有可能在个别情况下评估它们的重要性。从用户的角度来看，需要进行全面的模型性能评估以克服DES的成本，但所审查的DES模型中很少有报告这样的过程。更一般地说，将复杂模型与更简单模型进行进一步直接的实证比较，将能更好地了解使用DES来实现更复杂模型的益处以及最可能产生这种益处的情况。
