病例交叉设计和过度分散在空气污染流行病学中的应用。

IF 1.4 4区 数学 Q3 BIOLOGY Biometrics Pub Date : 2024-10-03 DOI:10.1093/biomtc/ujae117
Samuel Perreault, Gracia Y Dong, Alex Stringer, Hwashin Shin, Patrick E Brown
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引用次数: 0

摘要

在过去的三十年中,病例交叉设计在健康科学中得到了广泛应用,尤其是在空气污染流行病学中。它们通常与部分似然法技术相结合,用于定义以暴露为条件的反应(通常是健康结果)的条件逻辑模型。尽管在典型的空气污染流行病学设置中,条件 logistic 模型已被证明等同于著名的泊松时间序列模型的具体实例,但人们经常声称它们无法考虑过度分散。本文澄清了病例交叉设计、使用病例交叉设计所产生的模型与过度分散之间的关系。特别是,我们建议放宽个案交叉分析中传统的个体间独立性假设,以便在条件逻辑模型中明确引入过度分散。正如我们所展示的,由此产生的过度分散条件 logistic 模型与过度分散条件泊松模型相吻合,从这个意义上说,它们的似然值是彼此的简单再表达。我们进一步提供了贝叶斯法实现所提出的病例交叉模型的技术细节,并通过一项大型模拟研究证明,标准病例交叉模型会导致覆盖概率被严重低估,而所提出的模型不会。我们还对加拿大多伦多的空气污染与发病率之间的关系进行了说明性分析,结果表明,与标准模型相比,拟议模型对异常值(如与公共节假日相关的异常值)更稳健。
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Case-crossover designs and overdispersion with application to air pollution epidemiology.

Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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