Am J Epidemiol. 2021 Oct 1;190(10):2015-2018. doi: 10.1093/aje/kwab030.
Clinical prediction models (CPMs) are often used to guide treatment initiation, with individuals at high risk offered treatment. This implicitly assumes that the probability quoted from a CPM represents the risk to an individual of an adverse outcome in absence of treatment. However, for a CPM to correctly target this estimand requires careful causal thinking. One problem that needs to be overcome is treatment drop-in: where individuals in the development data commence treatment after the time of prediction but before the outcome occurs. In this issue of the Journal, Xu et al. (Am J Epidemiol. 2021;190(10):2000-2014) use causal estimates from external data sources, such as clinical trials, to adjust CPMs for treatment drop-in. This represents a pragmatic and promising approach to address this issue, and it illustrates the value of utilizing causal inference in prediction. Building causality into the prediction pipeline can also bring other benefits. These include the ability to make and compare hypothetical predictions under different interventions, to make CPMs more explainable and transparent, and to improve model generalizability. Enriching CPMs with causal inference therefore has the potential to add considerable value to the role of prediction in healthcare.
临床预测模型（CPMs）常用于指导治疗的启动，对高风险个体给予治疗。这隐含地假设，CPM 中给出的概率代表了在没有治疗的情况下，个体发生不良结局的风险。然而，为了使 CPM 正确地针对这一目标估计量，需要进行仔细的因果思考。需要克服的一个问题是治疗插入：即开发数据中的个体在预测后但在结局发生之前开始治疗。在本期《美国流行病学杂志》上，Xu 等人（Am J Epidemiol. 2021;190(10):2000-2014）使用来自临床试验等外部数据源的因果估计来调整 CPM 以适应治疗插入。这是解决这一问题的一种实用且有前途的方法，它说明了在预测中利用因果推理的价值。在预测管道中构建因果关系还可以带来其他好处。这些好处包括能够在不同干预措施下进行和比较假设预测，使 CPM 更具解释性和透明性，并提高模型的通用性。因此，用因果推理丰富 CPM 有可能为预测在医疗保健中的作用带来巨大的价值。
