在临床预测模型管理中使用留出集的伦理考量
Ethical considerations of use of hold-out sets in clinical prediction model management
AI总结：
本文探讨了在临床预测模型管理中使用留出集以应对表演性预测问题的伦理考量，重点分析了行善、不伤害、自主和公正等原则，并为研究人员提供了实施留出集的实用建议。
摘要：
临床预测模型是统计或机器学习模型，用于利用患者数据量化特定健康结果的风险。这些模型随后可为潜在的患者干预措施提供信息，从而引发一种称为表演性预测的效应：预测指导干预，而干预又会影响它们试图预测的结果，如果基于此类数据更新模型，可能导致对部分患者的风险估计不足。针对此问题的一种建议解决方案是使用留出集，即一组患者不接受模型衍生的风险评分，从而确保模型可以安全地进行重新训练。我们概述了在医疗环境中针对临床预测模型实施留出集的临床与研究伦理，重点关注行善、不伤害、自主和公正等伦理原则，并探讨了知情同意、临床均势与诚实告知。我们提供了留出集潜在实施的说明性案例，并讨论了不同留出集抽样方法引发的统计问题，以及留出集与随机对照试验在伦理和统计问题上的差异。最后，我们为有意在临床预测模型中使用留出集的研究人员提供了实用建议。
英文摘要：
Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.
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