Observability and its impact on differential bias for clinical prediction models.
Observability and its impact on differential bias for clinical prediction models.
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可观察性及其对临床预测模型差异偏差的影响。
DOI：
10.1093/jamia/ocac019
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Goldstein,BenjaminA
中科院分区：
文献类型：
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作者：
Yan,Mengying;Pencina,MichaelJ;Boulware,LEbony;Goldstein,BenjaminA
目的电子健康记录不能完整地记录患者的结果。我们考虑可观测性在预报器上是可微的情况。包括这样的预测器(敏感变量)可能会导致算法偏差，潜在地加剧健康不平等。材料和方法我们将临床预测模型(CPM)的偏差定义为真实风险和估计风险之间的差异，差异偏差定义为跨敏感变量不同的偏差。我们通过两个阶段的过程来说明差异偏差的起源，其中条件是有感兴趣的结果，结果被差异地观察。结果如果基于敏感变量的微分可观测性存在，则包含在CPM中的敏感变量会导致微分偏差。然而，如果敏感变量影响结果而不是可观察性，最好将其包括在内。当一个敏感变量同时影响可观察性和结果时，不能提供简单的建议。我们表明，人们不能使用观测数据来检测差分偏差。讨论我们的研究进一步发展了关于可观性的文献，表明差分可观测性会导致算法偏差。这突出了考虑是否将敏感变量包括在CPM中的重要性。结论将敏感变量包括在CPM中取决于它是真正影响结果还是仅仅影响结果的可观性。由于这不能与观测数据区分开来，因此可观测性是CPM的隐含假设。
ObjectiveElectronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.Materials and MethodsWe define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM.ResultsIf there is differential observability based on a sensitive variable, including it in a CPM can induce differential bias. However, if the sensitive variable impacts the outcome but not observability, it is better to include it. When a sensitive variable impacts both observability and the outcome no simple recommendation can be provided. We show that one cannot use observed data to detect differential bias.DiscussionOur study furthers the literature on observability, showing that differential observability can lead to algorithmic bias. This highlights the importance of considering whether to include sensitive variables in CPMs.ConclusionIncluding a sensitive variable in a CPM depends on whether it truly affects the outcome or just the observability of the outcome. Since this cannot be distinguished with observed data, observability is an implicit assumption of CPMs.