1. Teaching DAGs to support MBChB students design, analyze and critically appraise clinical research
George TH Ellison PhD DSc
Division of Epidemiology and Biostatistics
Leeds Institute of Genetics, Health and Therapeutics
Wendy Harrison (Leeds) and Graham Law (Leeds)
Johannes Textor (Utrecht) 1

2. Why teach statistical modeling in MBChB?
Most clinical research/audit uses an observational design
Most observational research is poorly/implicitly modelled
Why DAGs?
 DAGs help us distinguish between:
- nonparametric theoretical models of causality; and
- optimal parametric statistical models for testing these
 DAGs can be used at every stage of quantitative research:
- optimising the number of variables measured (design)
- optimising adjustment for confounding (analysis)
- evaluating published statistical models (critical appraisal) 2

3. What is a DAG (Directed Acyclic Graph)?
 A type of ‘causal path diagram’ with: unidirectional (‘causal’) arrows linking variables; and no circular paths 3

4. Challenges facing the application of DAGs
Algorithms are tedious and time-consuming to apply
DAGs with more than a handful of variables are complex
DAGitty.net applies algorithms automatically 4

5.  no causal relationship
Cross-tabulation might help as variables 
causes one above
 caused by one above
 no causal relationship 5

6. Comparing three ways of drawing DAGs
Three one-hour tutorials using three approaches:
(i) ‘graphical’; (ii) ‘cross-tabulation’; and (iii) ‘relational’
 Each approach evaluated based on:
- how many variables were included in the DAG
- mediators/confounders correctly identified*
- student feedback on ease of use and interpretation
 All participants were third year MBChB students who had
completed a year-long critical appraisal course
 The context was a published paper on an accessible topic:
‘determinants of pregnancy-associated weight gain’ 6

7. 7

8. 8

9. Handouts contained 10, 20 and 30 variables:9

10. Group using ‘graphical’ approach:10

11. Group using ‘cross-tabulation’ approach:11

12. Group using ‘cross-tabulation’ approach:12

13. Group using ‘relational’ approach:13

14. Group using ‘relational’ approach:14

15. What do I (now) think the DAG should be?15

16. Focusing on the ‘relational’
None of the students were able to attempt including more
than 10 variables in their cross-tabulation
86% correctly identified covariates that should have been
classified as ‘mediators’ by Harris et al
Fewer than 5% correctly identified the only covariate that
is likely to have acted as ‘confounder’ (maternal age)
A disproportionate use of ‘competing exposure’ as a
classification for covariates that are likely to have been
‘mediators’ suggests students were reluctant to identify
‘exposure’ as a potential/likely/theoretical cause 16

17. 17

18. Focusing on the ‘relational’
Most students found it ‘Difficult’...
 Why?
- understanding DAGs and DAG-related terminology
- “Time consuming” debating/agreeing links and directions
- “...so much depends on variation and opinion” 18

19. Summary It is feasible to teach DAGs to MBChB students
 Most students are capable of distinguishing between
‘confounders’, ‘mediators’ and ‘competing exposures’
 ‘Cross-tabulation’ and ‘relational’ were slower to apply
but less likely to result in errors
 Suggestions for future development:
- include a quiz to strengthen initial knowledge
- (perhaps) avoid group work (at least initially)
- reward recognition of ‘subjective causality’
- explore an approach that involves removing rather than
including causal paths (‘arcs’) 19