Software Application Profile: The daggle app-a tool to support learning and teaching the graphical rules of selecting adjustment variables using directed acyclic graphs

被引：0

作者：

Hanly, Mark ^{[1
,5
]}

Brew, Bronwyn K. ^{[1
,2
]}

Austin, Anna ^{[3
,4
]}

Jorm, Louisa ^{[1
]}

机构：

[1] UNSW Sydney, Ctr Big Data Res Hlth, Sydney, NSW, Australia

[2] UNSW Sydney, Sch Clin Med, Natl Perinatal Epidemiol & Stat Unit, Sydney, NSW, Australia

[3] Univ North Carolina Chapel Hill, Gillings Sch Global Publ Hlth, Dept Maternal & Child Hlth, Chapel Hill, NC USA

[4] Univ North Carolina Chapel Hill, Injury Prevent Res Ctr, Chapel Hill, NC USA

[5] UNSW Sydney, Ctr Big Data Res Hlth, Level 2,AGSM G27, Sydney, NSW 2052, Australia

来源：

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY | 2023年 / 52卷 / 05期

关键词：

Directed acyclic graphs; teaching; confounding adjustment; CAUSAL INFERENCE; DIAGRAMS;

D O I：

10.1093/ije/dyad038

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Motivation 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.General features 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.Implementation 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.

引用

页码：1659 / 1664

页数：6