论文标题
关于“利益的一致性统计”的方法论问题，作为治疗福利预测歧视的衡量标准
Methodological concerns about 'concordance-statistic for benefit' as a measure of discrimination in treatment benefit prediction
论文作者
论文摘要
量化给定治疗条件的预期益处的预测算法可以严重提出医疗决定。量化治疗益处预测算法的性能是一个积极的研究领域。最近提出的指标，即福利的一致性统计数据（CFB），通过直接将一致性统计量的概念从具有二进制结果的风险模型扩展到治疗福利模型的风险模型来评估治疗福利预测因子的歧视能力。在这项工作中，我们在多个方面仔细检查了$ CFB $。通过数值示例和理论发展，我们表明CFB不是适当的评分规则。我们还表明，它对反事实和匹配对的定义之间的不可估量的相关性很敏感。我们认为，适用于预测福利的统计分散量的度量不会遭受这些问题的困扰，并且可以作为治疗福利预测指标的歧视性能的替代指标。
Prediction algorithms that quantify the expected benefit of a given treatment conditional on patient characteristics can critically inform medical decisions. Quantifying the performance of treatment benefit prediction algorithms is an active area of research. A recently proposed metric, the concordance statistic for benefit (cfb), evaluates the discriminative ability of a treatment benefit predictor by directly extending the concept of the concordance statistic from a risk model with a binary outcome to a model for treatment benefit. In this work, we scrutinize $cfb$ on multiple fronts. Through numerical examples and theoretical developments, we show that cfb is not a proper scoring rule. We also show that it is sensitive to the unestimable correlation between counterfactual outcomes and to the definition of matched pairs. We argue that measures of statistical dispersion applied to predicted benefits do not suffer from these issues and can be an alternative metric for the discriminatory performance of treatment benefit predictors.
