Dijk Stijntje W, Korf Maurice, Labrecque Jeremy A, Pandya Ankur, Ferket Bart S, Hallsson Lára R, Wong John B, Siebert Uwe, Hunink M G Myriam
Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Med Decis Making. 2025 Apr;45(3):223-231. doi: 10.1177/0272989X241310898. Epub 2025 Jan 23.
Decision-analytic models (DAMs) are essentially informative yet complex tools for solving questions in medical decision making. When their complexity grows, the need for causal inference techniques becomes evident as causal relationships between variables become unclear. In this methodological commentary, we argue that graphical representations of assumptions on such relationships, directed acyclic graphs (DAGs), can enhance the transparency of decision models and aid in parameter selection and estimation through visually specifying backdoor paths (i.e., potential biases in parameter estimates) and visually clarifying structural modeling choices of frontdoor paths (i.e., the effect of the model structure on the outcome). This commentary discusses the benefit of integrating DAGs and DAMs in medical decision making and in particular health economics with 2 applications: the first examines statin use for prevention of cardiovascular disease, and the second considers mindfulness-based interventions for students' stress. Despite the potential application of DAGs in the decision science framework, challenges remain, including simplicity, defining the scope of a DAG, unmeasured confounding, noncausal aspects, and limited data availability or quality. Broader adoption of DAGs in decision science requires full-model applications and further debate.HighlightsOur commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.
决策分析模型（DAMs）本质上是用于解决医学决策问题的信息丰富但复杂的工具。当它们的复杂性增加时，随着变量之间的因果关系变得不明确，对因果推断技术的需求就变得显而易见。在这篇方法学评论中，我们认为，关于此类关系的假设的图形表示，即有向无环图（DAGs），可以提高决策模型的透明度，并通过直观地指定后门路径（即参数估计中的潜在偏差）和直观地阐明前门路径的结构建模选择（即模型结构对结果的影响）来帮助进行参数选择和估计。本评论讨论了在医学决策，特别是健康经济学中整合DAGs和DAMs的好处，并给出两个应用实例：第一个实例考察他汀类药物用于预防心血管疾病的情况，第二个实例考虑基于正念的干预措施对学生压力的影响。尽管DAGs在决策科学框架中有潜在应用，但挑战依然存在，包括简单性、定义DAG的范围、未测量的混杂因素、非因果方面以及数据可用性或质量有限等。DAGs在决策科学中的更广泛采用需要完整模型的应用和进一步的讨论。要点我们的评论提议将有向无环图（DAGs）应用于决策分析模型的设计，为研究人员提供一个有价值的结构化工具，通过弥合医学决策中因果推断与模型设计之间的差距来提高透明度和准确性。本文中的实际例子展示了DAGs对模型结构、参数选择以及由此得出的有效性和成本效益结论可能产生的变革性影响。这篇方法学文章引发了关于基于因果假设的决策建模选择的更广泛讨论。
