On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework
Authors' conclusion
The implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short.
Study context
- Exposure
- Weight loss/gain vs maintenance
- Outcome
- Recommended analytic strategy
Methodological detailstarget trial emulation framework, simulated observational data, hypothetical randomised trial example
- Target trial emulation framework
- Simulated observational data
- Hypothetical randomised trial example
Main finding
As reported in the abstract
- Recommended method defines time-zero at enrolment, with adjustment for confounders (or exclusion based on confounder levels) at both enrolment and the 1st follow-up
Observational data: associations, not effects.
Original publication
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