半连续结果常见于众多领域，如保险理赔、医疗支出、降雨量和酒精消费量。回归模型（包括Tobit、Tweedie和两部分模型）被广泛用于理解半连续结果与协变量之间的关系。鉴于模型误设的潜在不利后果，在拟合回归模型后，检查模型的充分性至关重要。然而，由于在零处存在点质量，标准的回归模型诊断工具（如偏差残差和Pearson残差）对半连续数据缺乏信息量。为了弥补这一空白，我们提出了一种适用于一般回归模型的半连续结果新残差。在正确设定的模型下，所提出的残差收敛于均匀分布；当模型被误设时，它们会显著偏离此模式。除样本内验证外，所提方法也可用于评估预测分布。我们使用美国医疗支出面板调查（US Medical Expenditure Panel Survey）的医疗支出数据证明了所提工具的有效性。
英文摘要：
Semicontinuous outcomes commonly arise in a wide variety of fields, such as insurance claims, healthcare expenditures, rainfall amounts, and alcohol consumption. Regression models, including Tobit, Tweedie, and two-part models, are widely employed to understand the relationship between semicontinuous outcomes and covariates. Given the potential detrimental consequences of model misspecification, after fitting a regression model, it is of prime importance to check the adequacy of the model. However, due to the point mass at zero, standard diagnostic tools for regression models (e.g., deviance and Pearson residuals) are not informative for semicontinuous data. To bridge this gap, we propose a new type of residuals for semicontinuous outcomes that are applicable to general regression models. Under the correctly specified model, the proposed residuals converge to being uniformly distributed, and when the model is misspecified, they significantly depart from this pattern. In addition to in-sample validation, the proposed methodology can also be employed to evaluate predictive distributions. We demonstrate the effectiveness of the proposed tool using health expenditure data from the US Medical Expenditure Panel Survey.
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计系）
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