Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW, Australia.
National Perinatal Epidemiology and Statistics Unit, School of Clinical Medicine, UNSW Sydney, Sydney, NSW, Australia.
Int J Epidemiol. 2023 Oct 5;52(5):1659-1664. doi: 10.1093/ije/dyad038.
Directed acyclic graphs (DAGs) are used in epidemiological research to communicate causal assumptions and guide the selection of covariate adjustment sets when estimating causal effects. For any given DAG, a set of graphical rules can be applied to identify minimally sufficient adjustment sets that can be used to adjust for bias due to confounding when estimating the causal effect of an exposure on an outcome. The daggle app is a web-based application that aims to assist in the learning and teaching of adjustment set identification using DAGs.
The application offers two modes: tutorial and random. The tutorial mode presents a guided introduction to how common causal structures can be presented using DAGs and how graphical rules can be used to identify minimally sufficient adjustment sets for causal estimation. The random mode tests this understanding by presenting the user with a randomly generated DAG-a daggle. To solve the daggle, users must correctly identify a valid minimally sufficient adjustment set.
The daggle app is implemented as an R shiny application using the golem framework. The application builds upon existing R libraries including pcalg to generate reproducible random DAGs, dagitty to identify all valid minimal adjustment sets and ggdag to visualize DAGs.
The daggle app can be accessed online at [http://cbdrh.shinyapps.io/daggle]. The source code is available on GitHub [https://github.com/CBDRH/daggle] and is released under a Creative Commons CC BY-NC-SA 4.0 licence.
有向无环图 (DAG) 用于流行病学研究中，用于传达因果假设，并在估计因果效应时指导协变量调整集的选择。对于任何给定的 DAG，可以应用一组图形规则来识别最小充分调整集，这些调整集可用于调整因混杂引起的偏差，以估计暴露对结果的因果效应。daggle 应用程序是一个基于网络的应用程序，旨在帮助学习和教授使用 DAG 进行调整集识别。
该应用程序提供两种模式：教程和随机。教程模式提供了一个关于如何使用 DAG 呈现常见因果结构以及如何使用图形规则识别用于因果估计的最小充分调整集的指导介绍。随机模式通过向用户呈现一个随机生成的 DAG-a daggle 来测试这种理解。要解决 daggle，用户必须正确识别有效的最小充分调整集。
daggle 应用程序是使用 golem 框架作为 R shiny 应用程序实现的。该应用程序建立在现有的 R 库之上，包括 pcalg 来生成可重现的随机 DAG、dagitty 来识别所有有效的最小调整集和 ggdag 来可视化 DAG。
daggle 应用程序可以在线访问[http://cbdrh.shinyapps.io/daggle]。源代码可在 GitHub[https://github.com/CBDRH/daggle]上获得，并根据知识共享 CC BY-NC-SA 4.0 许可证发布。
