Ali, R., Prestwich, A., Ge, J. orcid.org/0000-0001-6491-3851 et al. (6 more authors) (2025) Composite variable bias: causal analysis of weight outcomes. International Journal of Obesity. ISSN 0307-0565
Abstract
Background Researchers often use composite variables (e.g., BMI and change scores). By combining multiple variables (e.g., height and weight or follow-up weight and baseline weight) into a single variable it becomes challenging to untangle the causal roles of each component variable. Composite variable bias—an issue previously identified for exposure variables that may yield misleading causal inferences—is illustrated as a similar concern for composite outcomes. We explain how this occurs for composite weight outcomes: BMI, ‘weight change’, their combination ‘BMI change’, and variations involving relative change.
Methods Data from the National Child Development Study (NCDS) cohort surveys (n = 9223) were analysed to estimate the causal effect of ethnicity, sex, economic status, malaise score, and baseline height/weight at age 23 on weight-related outcomes at age 33. The analyses were informed by a directed acyclic graph (DAG) to demonstrate the extent of composite variable bias for various weight outcomes.
Results Estimated causal effects differed across different weight outcomes. The analyses of follow-up BMI, ‘weight change’, ‘BMI change’, or relative change in body size yielded results that could lead to potentially different inferences for an intervention.
Conclusions 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jan 2025 09:33 |
Last Modified: | 24 Mar 2025 14:40 |
Status: | Published online |
Publisher: | Springer Nature |
Identification Number: | 10.1038/s41366-025-01732-6 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:222501 |