A case-crossover analysis is used as a simple but powerful tool for estimating the effect of short-term environmental factors such as extreme temperatures or poor air quality on mortality. The environment on the day of each death is compared to the one or more “control days” in previous weeks, and higher levels of exposure on death days than control days provide evidence of an effect. Current state-of-the-art methodology and software (integrated nested Laplace approximation [INLA]) cannot be used to fit the most flexible case-crossover models to large datasets, because the likelihood for case-crossover models cannot be expressed in a manner compatible with this methodology. In this paper, we develop a flexible and scalable modeling framework for case-crossover models with linear and semiparametric effects which retains the flexibility and computational advantages of INLA. We apply our method to quantify nonlinear associations between mortality and extreme temperatures in India. An R package implementing our methods is publicly available.

中文翻译：

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案例交叉模型的近似贝叶斯推理

病例交叉分析被用作一种简单但强大的工具，用于估计短期环境因素（如极端温度或空气质量差）对死亡率的影响。将每次死亡当天的环境与前几周的一个或多个“对照日”进行比较，死亡日比对照日更高的暴露水平提供了影响的证据。当前最先进的方法和软件（集成嵌套拉普拉斯近似 [INLA]）不能用于将最灵活的案例交叉模型拟合到大型数据集，因为案例交叉模型的可能性无法以某种方式表达符合这种方法论。在本文中，我们为具有线性和半参数效应的案例交叉模型开发了一个灵活且可扩展的建模框架，保留了 INLA 的灵活性和计算优势。我们应用我们的方法来量化印度死亡率与极端温度之间的非线性关联。一个实现我们方法的 R 包是公开的。