School of Social and Community Medicine, University of Bristol, Bristol, UK.
Medical Research Council Biostatistics Unit, Cambridge, UK.
Pharmacoeconomics. 2017 Sep;35(9):951-962. doi: 10.1007/s40273-017-0501-9.
This article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid.
We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate.
We illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease.
State structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.
本文探讨了在成本效益多状态模型中选择状态结构的问题。关键模型输出，如治疗建议和未来研究的优先级，可能对状态结构的选择敏感。例如，不确定是否应将类似的疾病严重程度或类似的临床事件视为同一状态或不同状态。比较模型的标准统计方法需要一个共同的参考数据集，但在模型中合并状态会汇总数据，从而使这些方法无效。
我们提出了一种方法，即将合并状态的模型重新表示为具有更大状态空间的模型，其中特定的转移概率、成本和效用在状态之间受到约束相等。这产生了一个与合并状态的模型具有相同成本效益估计的模型，同时保持数据不变。可以通过比较约束和非约束模型之间的最大似然或信息准则来比较状态结构。因此，我们可以检验两个状态中患者的成本和/或健康后果是否相同，从而判断是否可以合并状态。我们注意到，适当时可以为率、成本和效用使用不同的结构。
我们用最近评估抗抑郁药物按抑郁严重程度开具处方的成本效益和冠状动脉疾病诊断测试的成本效益的两个模型来说明我们的方法。
可以使用标准方法来比较约束和非约束模型，以比较成本效益模型中的状态结构。
