Prediction models are increasingly proposed for guiding treatment decisions, but most fail to address the special role of treatments, leading to inappropriate use. This paper highlights the limitations of using standard prediction models for treatment decision support. We identify `causal blind spots' in three common approaches to handling treatments in prediction modelling: including treatment as a predictor, restricting data based on treatment status and ignoring treatments. When predictions are used to inform treatment decisions, confounders, colliders and mediators, as well as changes in treatment protocols over time may lead to misinformed decision-making. We illustrate potential harmful consequences in several medical applications. We advocate for an extension of guidelines for development, reporting and evaluation of prediction models to ensure that the intended use of the model is matched to an appropriate risk estimand. When prediction models are intended to inform treatment decisions, prediction models should specify upfront the treatment decisions they aim to support and target a prediction estimand in line with that goal. This requires a shift towards developing predictions under the specific treatment options under consideration (`predictions under interventions'). Predictions under interventions need causal reasoning and inference techniques during development and validation. We argue that this will improve the efficacy of prediction models in guiding treatment decisions and prevent potential negative effects on patient outcomes.

翻译：预测模型越来越多地被提出用于指导治疗决策，但大多数模型未能解决治疗的特定作用，导致使用不当。本文强调了使用标准预测模型支持治疗决策的局限性。我们识别了预测建模中处理治疗的三种常见方法中的“因果盲区”：将治疗作为预测变量包含在内、根据治疗状态限制数据以及忽略治疗。当预测用于指导治疗决策时，混杂因素、碰撞变量和中介变量，以及治疗协议随时间的变化可能导致决策信息误导。我们在几个医学应用中展示了潜在的有害后果。我们主张扩展预测模型的开发、报告和评估指南，以确保模型的预期用途与适当的风险估计量相匹配。当预测模型旨在指导治疗决策时，应预先说明其支持的治疗决策，并针对与目标一致的治疗决策估计量进行预测。这需要转向在考虑特定治疗方案下进行预测（“干预下的预测”）。干预下的预测在开发和验证过程中需要因果推理和因果推断技术。我们认为这将提高预测模型在指导治疗决策中的有效性，并防止对患者结局可能产生的负面影响。