论文标题
从算法到行动：改善患者护理需要因果关系
From algorithms to action: improving patient care requires causality
论文作者
论文摘要
在癌症研究中，有很大的兴趣建立和验证结果预测结果以支持治疗决策。但是，由于大多数结果预测模型是开发和验证的，而无需考虑治疗决策的因果方面，因此许多已发表的结果预测模型在用于决策时可能会造成伤害，尽管在验证研究中被发现准确。美国癌症联合委员会的预测模型验证指南和风险模型认可的清单并不能防止预测模型在开发和验证过程中准确但在用于决策时有害。我们解释了为什么是这种情况以及如何构建和验证对决策有用的模型。
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.
