A key challenge in building effective regression models for large and diverse populations is accounting for patient heterogeneity. An example of such heterogeneity is in health system risk modeling efforts where different combinations of comorbidities fundamentally alter the relationship between covariates and health outcomes. Accounting for heterogeneity arising combinations of factors can yield more accurate and interpretable regression models. Yet, in the presence of high-dimensional covariates, accounting for this type of heterogeneity can exacerbate estimation difficulties even with large sample sizes. To handle these issues, we propose a flexible and interpretable risk modeling approach based on semiparametric sufficient dimension reduction. The approach accounts for patient heterogeneity, borrows strength in estimation across related subpopulations to improve both estimation efficiency and interpretability, and can serve as a useful exploratory tool or as a powerful predictive model. In simulated examples, we show that our approach often improves estimation performance in the presence of heterogeneity and is quite robust to deviations from its key underlying assumptions. We demonstrate our approach in an analysis of hospital admission risk for a large health system and demonstrate its predictive power when tested on further follow-up data.

中文翻译：

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对具有结构异质性的人群进行充分降维

为大量不同人群建立有效回归模型的一个关键挑战是考虑患者的异质性。这种异质性的一个例子是在卫生系统风险建模工作中，不同的合并症组合从根本上改变了协变量与健康结果之间的关系。考虑因素组合产生的异质性可以产生更准确和可解释的回归模型。然而，在存在高维协变量的情况下，考虑到这种类型的异质性，即使样本量很大，也会加剧估计困难。为了处理这些问题，我们提出了一种基于半参数充分降维的灵活且可解释的风险建模方法。该方法考虑了患者的异质性，借用相关子群体的估计强度来提高估计效率和可解释性，并且可以作为有用的探索工具或强大的预测模型。在模拟示例中，我们表明我们的方法通常会在存在异质性的情况下提高估计性能，并且对于偏离其关键基础假设的情况非常稳健。我们在大型卫生系统的入院风险分析中展示了我们的方法，并在对进一步的后续数据进行测试时展示了其预测能力。我们表明，我们的方法通常会在存在异质性的情况下提高估计性能，并且对于偏离其关键基本假设的偏差非常稳健。我们在大型卫生系统的入院风险分析中展示了我们的方法，并在对进一步的后续数据进行测试时展示了其预测能力。我们表明，我们的方法通常会在存在异质性的情况下提高估计性能，并且对于偏离其关键基本假设的偏差非常稳健。我们在大型卫生系统的入院风险分析中展示了我们的方法，并在对进一步的后续数据进行测试时展示了其预测能力。