Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.

中文翻译：

---

链事件图的改编，以用于流行病学的病例对照研究。

病例对照研究在流行病学中用于试图发现疾病的原因，但是一项回顾性研究设计，已知患有不参与和回忆偏见的现象，这可以解释近年来流行率下降的原因。传统分析通常只报告给定暴露和二元疾病状况的优势比。链事件图是从事件树中获得的统计模型的图形表示，这些事件树已经在人工智能和统计中得到发展，并且直到最近才被流行病学文献所采用。它们是一种现代贝叶斯技术，可以使用凝聚的层次聚类算法将先验知识整合到数据分析中，该算法用于形成合适的链事件图。此外，他们可以解释缺失的数据，并用于探索缺失机制。在这里，我们调整链式事件图框架以适合案例对照研究中经常遇到的场景，以加强这种既省时又经济的研究设计。我们展示了对图形的八种适应方法，其中包括两种适用于完全病例对照研究分析的方法，四种可用于中期分析中探索偏见的方法，以及两种旨在提高分析的简便性和准确性的方法。通过完整，可重现，经过充分解释的示例说明了改编，包括事件树和链事件图。在这里，链事件图首次用于总结病例对照研究中的非参与，数据收集技术，数据可靠性和疾病严重性。