Composite variable bias: causal analysis of weight outcomes
This is the first study to illustrate that causal estimates on composite weight outcomes vary and can lead to potentially misleading inferences. It is recommended that only follow-up weight be analysed while conditioning on baseline weight for meaningful estimates. How conditioning on baseline weight is implemented depends on whether baseline weight precedes or follows the exposure of interest. For the former, conditioning on baseline weight may be achieved by inclusion in the regression model or via a propensity score. For the latter, alternative strategies are necessary to model the joint effects of the exposure and baseline weight-the choice of strategy can be informed by a DAG.
Authors
- Yu‐Wang Chen (ORCID: https://orcid.org/0000-0002-2007-1821)
- Claire Griffiths (ORCID: https://orcid.org/0000-0002-2588-1022)
- Mohammad Alì Mansournia (ORCID: https://orcid.org/0000-0003-3343-2718)
- Ridda Ali
- Mark S. Gilthorpe (ORCID: https://orcid.org/0000-0001-8783-7695)
- Andrew Prestwich (ORCID: https://orcid.org/0000-0002-7489-6415)
- Azar Shahgholian (ORCID: https://orcid.org/0000-0002-8551-2296)
- Richard Allmendinger (ORCID: https://orcid.org/0000-0003-1236-3143)
- Jiaqi Ge
Institutions
- Turing Institute (GB)
- University of Leeds (GB)
- University of Manchester (GB)
- The Alan Turing Institute (GB)
- Liverpool John Moores University (GB)
- Tehran University of Medical Sciences (IR)
- Leeds Beckett University (GB)
Publication Details
- Journal
- International Journal of Obesity
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1038/s41366-025-01732-6
- Citations
- 2
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.26
Funders
- Alan Turing Institute
- Economic and Social Research Council
- Institute of Education, University of London