风险评估的风险：使用预测模型做治疗决策时的因果盲点
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
AI总结：
该研究指出临床常用的三种预测模型治疗处理方法存在因果盲点，易引发误读与决策失误，建议扩展模型开发指南并引入因果推断技术，以保障治疗决策安全。
摘要：
临床医生越来越依赖预测模型来指导治疗选择。然而，大多数预测模型是使用观察性数据开发的，这些数据中包含了部分已经接受过预测模型本应提供参考的治疗的患者。在解读由此产生的预测结果时，需要特别关注这些前期治疗的因果作用。我们指出了开发预测模型时三种常见的处理治疗的方法存在的“因果盲点”：将治疗作为预测因子、限定于接受某种特定治疗的个体、以及忽略治疗因素。通过多个真实案例，我们阐释了使用这类方法开发的模型得出的风险值可能会被误读，并可能导致决策失误。我们的讨论涵盖了由混杂因素、选择偏倚、中介效应以及治疗方案随时间变化所引发的问题。我们主张扩展预测模型开发、报告和评估的相关指南，以避免此类误读。开发者必须确保清晰传达模型的预期目标人群，以及预测结果成立的治疗条件。当预测模型旨在为治疗决策提供参考时，它们需要提供所考虑的特定治疗（或干预）选项下的风险估计值，即“干预下的预测”。除了合适的数据外，这还需要在模型开发和评估过程中运用因果推理和因果推断技术。明确给定预测模型的适用和不适用场景，能够避免错误的治疗决策，从而防止对患者造成潜在伤害。
英文摘要：
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions. We identify 'causal blind spots' in three common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to individuals taking a certain treatment, and ignoring treatment. Through several real examples, we illustrate how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision-making. Our discussion covers issues attributable to confounding, selection, mediation and changes in treatment protocols over time. We advocate for an extension of guidelines for the development, reporting and evaluation of prediction models to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as 'prediction under interventions'. Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.
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