ABSTRACT Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this “risk adjustment” system is to convey incentives to health plans such that they provide health care services fairly, a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R2 for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric.

中文翻译：

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逐步回归构建的 P 值和 R 平方的局限性：卫生政策风险调整中的公平性证明

摘要逐步回归构建程序是常用的应用统计工具，尽管它们有众所周知的缺点。虽然文献中已经广泛讨论了它们的许多局限性，但使用单个统计拟合度量的其他方面，尤其是在高维逐步回归设置中，还没有。像 p 值和 R2 一样，优先考虑个体拟合，当群体拟合可能是更大的关注点时，可能会导致错误的决策。逐步回归最重要的用途之一是在医疗保健中，这些工具将数千亿美元分配给健康计划，以招募具有不同预测医疗保健费用的个人。这种“风险调整”系统的主要目标是向健康计划传达激励措施，以便它们公平地提供医疗保健服务，其中一个组成部分是不歧视可能昂贵的个人或群体的访问或照顾。我们通过额外考虑组级公平度量，通过一个说明性示例解决了此政策问题中高维逐步回归的 p 值和 R2 的一些特定限制。