Department of Public Health Sciences, University of Lige, Lige, Belgium.
Pharmacoeconomics. 2012 Mar;30(3):171-81. doi: 10.2165/11593050-000000000-00000.
While no single type of model can provide adequate answers under all circumstances, any modelling endeavour should incorporate three fundamental considerations in any decision-making question: the target population, the disease and the intervention characteristics. A target population is likely to be characterized by various types of heterogeneity and a dynamic evolution over time. It is therefore important to adequately capture these population effects on the results of a model. There are essentially two different approaches in modelling a population over time: a cohort-based approach and a population-based approach. In a cohort-based model, a closed group of individuals who have at least one specific characteristic or experience in common over a defined period of time is run through a state transition process. The cohort is generally composed of a hypothetical number of representative or 'average' individuals (i.e. the target population is considered to be a homogeneous group). The population-based approach projects the evolution of the estimated prevalent target population and intends to reflect as much as possible the demographic, epidemiological and clinical characteristics of the prevalent target population relevant for the decision problem. A cohort-based approach is generally used in most published healthcare decision models. However, this choice is rarely discussed by modellers. In this article, we challenge this assumption. To address the underlying decision problem, we affirm it is crucial that modellers consider the characteristics of the target population. Then, they could opt for using the most appropriate approach. Decision makers should also understand the impact on the results of both types of models in order to make informed healthcare decisions.
虽然没有任何一种模型可以在所有情况下提供充分的答案，但任何建模工作都应该在任何决策问题中纳入三个基本考虑因素：目标人群、疾病和干预措施的特征。目标人群可能具有各种类型的异质性，并随着时间的推移发生动态演变。因此，重要的是要充分捕捉这些人群效应对模型结果的影响。基本上有两种不同的方法可以随时间对人群进行建模：基于队列的方法和基于人群的方法。在基于队列的模型中，通过状态转移过程来运行一个具有至少一个特定特征或共同经历的封闭人群。该队列通常由具有代表性或“平均”个体的假设数量组成（即目标人群被认为是同质群体）。基于人群的方法则预测估计的现有目标人群的演变，并尽可能反映与决策问题相关的现有目标人群的人口统计学、流行病学和临床特征。基于队列的方法通常用于大多数已发表的医疗保健决策模型中。然而，建模者很少讨论这种选择。在本文中，我们对这一假设提出质疑。为了解决潜在的决策问题，我们肯定建模者考虑目标人群的特征至关重要。然后，他们可以选择使用最合适的方法。决策者还应该了解这两种模型对结果的影响，以便做出明智的医疗保健决策。
