Abstract：
OBJECTIVE: The American College of Cardiology and the American Heart Association guidelines on primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend using 10-year ASCVD risk estimation models to initiate statin treatment. For guideline-concordant decision-making, risk estimates need to be calibrated. However, existing models are often miscalibrated for race, ethnicity and sex based subgroups. This study evaluates two algorithmic fairness approaches to adjust the risk estimators (group recalibration and equalised odds) for their compatibility with the assumptions underpinning the guidelines\' decision rules.MethodsUsing an updated pooled cohorts data set, we derive unconstrained, group-recalibrated and equalised odds-constrained versions of the 10-year ASCVD risk estimators, and compare their calibration at guideline-concordant decision thresholds.
RESULTS: We find that, compared with the unconstrained model, group-recalibration improves calibration at one of the relevant thresholds for each group, but exacerbates differences in false positive and false negative rates between groups. An equalised odds constraint, meant to equalise error rates across groups, does so by miscalibrating the model overall and at relevant decision thresholds.
CONCLUSIONS: Hence, because of induced miscalibration, decisions guided by risk estimators learned with an equalised odds fairness constraint are not concordant with existing guidelines. Conversely, recalibrating the model separately for each group can increase guideline compatibility, while increasing intergroup differences in error rates. As such, comparisons of error rates across groups can be misleading when guidelines recommend treating at fixed decision thresholds.
CONCLUSIONS: The illustrated tradeoffs between satisfying a fairness criterion and retaining guideline compatibility underscore the need to evaluate models in the context of downstream interventions.
RESULTS: We find that, compared with the unconstrained model, group-recalibration improves calibration at one of the relevant thresholds for each group, but exacerbates differences in false positive and false negative rates between groups. An equalised odds constraint, meant to equalise error rates across groups, does so by miscalibrating the model overall and at relevant decision thresholds.
CONCLUSIONS: Hence, because of induced miscalibration, decisions guided by risk estimators learned with an equalised odds fairness constraint are not concordant with existing guidelines. Conversely, recalibrating the model separately for each group can increase guideline compatibility, while increasing intergroup differences in error rates. As such, comparisons of error rates across groups can be misleading when guidelines recommend treating at fixed decision thresholds.
CONCLUSIONS: The illustrated tradeoffs between satisfying a fairness criterion and retaining guideline compatibility underscore the need to evaluate models in the context of downstream interventions.
摘要：
目的：美国心脏病学会和美国心脏协会动脉粥样硬化性心血管疾病（ASCVD）一级预防指南推荐使用10年ASCVD风险评估模型来启动他汀类药物治疗。对于准则一致的决策，风险估计需要校准。然而,现有的模型经常针对种族进行错误校准，基于种族和性别的亚组。本研究评估了两种算法公平性方法，以调整风险估计器（组重新校准和均衡赔率），使其与指导准则决策规则的假设兼容。方法使用更新的汇总队列数据集，我们推导出无约束，10年ASCVD风险估计器的组重新校准和均衡赔率约束版本，并将它们的校准值与指南一致的决策阈值进行比较。
结果：我们发现，与无约束模型相比，组重新校准改进了每个组的相关阈值之一的校准，但加剧了组间假阳性和假阴性率的差异。均衡赔率约束，旨在均衡各组的错误率，这样做是通过错误校准整个模型和相关的决策阈值。
结论：因此，由于诱发的校准错误，由风险估计器指导的决策与均衡的赔率公平约束不一致的现有准则。相反，为每个组分别重新校准模型可以增加指南兼容性，同时增加了错误率的组间差异。因此，当指南建议以固定的决策阈值进行治疗时,组间错误率的比较可能会产生误导.
结论：说明的满足公平性标准和保持指南兼容性之间的权衡强调了在下游干预背景下评估模型的必要性。
结果：我们发现，与无约束模型相比，组重新校准改进了每个组的相关阈值之一的校准，但加剧了组间假阳性和假阴性率的差异。均衡赔率约束，旨在均衡各组的错误率，这样做是通过错误校准整个模型和相关的决策阈值。
结论：因此，由于诱发的校准错误，由风险估计器指导的决策与均衡的赔率公平约束不一致的现有准则。相反，为每个组分别重新校准模型可以增加指南兼容性，同时增加了错误率的组间差异。因此，当指南建议以固定的决策阈值进行治疗时,组间错误率的比较可能会产生误导.
结论：说明的满足公平性标准和保持指南兼容性之间的权衡强调了在下游干预背景下评估模型的必要性。
