Composite variable bias: causal analysis of weight outcomes
Authors' conclusion
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.
Study context
- Population
- National Child Development Study (NCDS) cohort; exposures at age 23 (ethnicity, sex, economic status, malaise score, baseline height/weight), weight-related outcomes at age 33NCDS cohort
- Participants
- 9,223 participants
- Outcome
- Causal effects across composite weight outcomes
- Follow-up
- Age 33
Methodological detailsdirected acyclic graph (DAG)-informed causal analysis
- Directed acyclic graph (DAG)-informed causal analysis
Main finding
As reported in the abstract
- Estimated causal effects differed across different weight outcomes
- 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
Observational data: associations, not effects.
Original publication
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