Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning
Multidimensional outcome constructs formed under a formative model increases the complexity of causal analyses. Despite this, multidimensional measures may particularly aid with a variety of 'outcome-wide' studies when assessing exposures that may be beneficial for some outcomes but harmful for others. Thus, we have taken important steps to supporting such studies in observational settings by showing how PROMs can be incorporated into DAGs to inform such causal analyses.
Authors
- Tessa Peasgood (ORCID: https://orcid.org/0000-0001-8024-7801)
- Peter W. G. Tennant (ORCID: https://orcid.org/0000-0003-1555-069X)
- Matthew Franklin (ORCID: https://orcid.org/0000-0002-2774-9439)
Institutions
- Turing Institute (GB)
- University of Leeds (GB)
- British Library (GB)
- University of Sheffield (GB)
Publication Details
- Journal
- Quality of Life Research
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1007/s11136-025-04007-9
- Citations
- 2
- Primary Topic
- Health Systems, Economic Evaluations, Quality of Life
- Type
- article
- Field-Weighted Citation Impact
- 6.26
Funders
- National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber