Franklin, M. orcid.org/0000-0002-2774-9439, Peasgood, T. orcid.org/0000-0001-8024-7801 and Tennant, P.W.G. orcid.org/0000-0003-1555-069X (2025) Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning. Quality of Life Research. ISSN 0962-9343
Abstract
Purpose
Estimating causal effects of an exposure (e.g., health condition or treatment) on a patient-reported outcome measure (PROM) can have complications depending on the relationship between the PROM’s indicators and construct(s). Using directed acyclic graphs (DAGs) as visual tools, we show how to represent a PROM’s potential internal causal relationship between its indicators and latent construct(s), then explain the implications when also accounting for external variables when estimating causal effects within observational data.
Methods
Measurement theory suggests a PROM’s relationships between its items/indicators and latent construct(s) is reflective (construct causes the indicators) or formative (indicators cause the construct). We present DAGs under reflective and formative model assumptions when the PROM is unidimensional (e.g., Patient Health Questionnaire-9 [PHQ-9] representing depression severity) or multidimensional (e.g., EQ-5D representing health-related quality-of-life).
Results
Unidimensional PROMs under a reflective model can be analysed like other unidimensional outcomes (e.g., mortality) to estimate causal effects, thus don’t require additional consideration. In comparison, each indicator of a multidimensional construct under a formative model needs specific consideration to ensure relevant external variables are appropriately conditioned to estimate causal effects.
Conclusion
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Formal Reasoning; Mixed Methods; Outcomes research; Predictive markers; Reasoning; Social Indicators |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number DEPARTMENT OF HEALTH AND SOCIAL CARE UNSPECIFIED DEPARTMENT OF HEALTH AND SOCIAL CARE NIHR200166 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Jul 2025 13:08 |
Last Modified: | 01 Jul 2025 13:11 |
Status: | Published online |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1007/s11136-025-04007-9 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:228594 |