george th ellison phd dsc division of epidemiology and biostatistics leeds institute of genetics,...
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![Page 1: George TH Ellison PhD DSc Division of Epidemiology and Biostatistics Leeds Institute of Genetics, Health and Therapeutics g.t.h.ellison@leeds.ac.uk Wendy](https://reader036.vdocument.in/reader036/viewer/2022062516/56649d755503460f94a5520b/html5/thumbnails/1.jpg)
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)
Teaching DAGs to support MBChB students design, analyze and critically appraise
clinical research
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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)
Why DAGs?
Why teach statistical modeling in MBChB? Most clinical research/audit uses an observational design Most observational research is poorly/implicitly modelled
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What is a DAG (Directed Acyclic Graph)? A type of ‘causal path diagram’ with: unidirectional
(‘causal’) arrows linking variables; and no circular paths
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DAGitty.net applies algorithms automatically
Challenges facing the application of DAGs Algorithms are tedious and time-consuming to apply DAGs with more than a handful of variables are complex
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Cross-tabulation might help as variables
causes one above
caused by one above
no causal relationship
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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’
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Handouts contained 10, 20 and 30 variables:
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Group using ‘graphical’ approach:
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Group using ‘cross-tabulation’ approach:
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Group using ‘cross-tabulation’ approach:
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Group using ‘relational’ approach:
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Group using ‘relational’ approach:
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What do I (now) think the DAG should be?
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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. 1999... 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
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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”
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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’)