With strong involvement from individuals with a lived experience of chronic pain, IMI researchers Dr Beate Ehrhardt and Dr Laura Oporto Lisboa, worked in an EU-wide collaboration to develop tools that would improve the lives of people suffering with chronic pain.
The state of an individual’s pain fluctuates over time, and across situations. Similarly, there is variation in risk and protective factors, and how they impact on these pain-related transitions. The cross-disciplinary team were interested in whether such variations were more than random, and whether they could be accounted for by observed variables.
Using available large longitudinal datasets such as the UK Biobank, there was a unique opportunity to study these variations at scale. To avoid the high risk of bias and danger of over-interpretation, it was critical to be transparent about causal thinking. This was made possible using Directed Acyclic Graphs (DAGs): graphical representations of the hypothesized causal relationships between variables. The DAGs were used to identify the smallest set of variables that need to be adjusted for to remove confounding bias in estimating the causal effect of an exposure on an outcome.
The use of DAGs in pain research is not common despite their potential to guide study design and data-analysis, but within the research the team constructed a workflow for building a DAG using domain knowledge from three different sources:
- researchers
- people with lived experience
- the literature
They were able to create a DAG for the putative effect of executive function on the maintenance of chronic high impact pain and utilise the DAG process to add critical clarity to the analysis plan and downstream interpretation.
Read more about this research in their paper published by the Journal of the International Association for the Study of Pain.