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Variable selection when estimating effects in external target populations.

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Summary
This summary is machine-generated.

Including non-effect measure modifiers (non-EMMs) in epidemiologic research can reduce estimate precision. However, non-EMMs associated with selection do not worsen bias from omitting necessary effect measure modifiers (EMMs).

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • External validity is crucial for generalizing research findings to target populations.
  • Estimating effects in new populations requires careful consideration of effect measure modifiers (EMMs).
  • The impact of including non-EMMs in adjustment sets on these estimates is not well understood.

Purpose of the Study:

  • To evaluate how including non-EMMs affects the estimation of transported risk differences (RDs).
  • To assess the influence of covariates differing between populations, associated with outcomes, or modifying RDs on estimate precision and bias.

Main Methods:

  • Simulations were used to model the inclusion of non-EMMs with varying characteristics.
  • Covariates were analyzed based on differences between trial and target populations, outcome association, and RD modification.
  • Estimation methods included outcome modeling and inverse odds weighting.

Main Results:

  • Including non-EMMs that differ in distribution between populations reduced estimate precision, irrespective of outcome association.
  • Non-EMMs associated with selection did not exacerbate bias caused by omitting necessary EMMs.
  • Adjusting for all outcome-associated variables can lead to imprecise treatment effect estimates in external populations.

Conclusions:

  • Careful selection of adjustment variables is necessary for valid external validity estimation.
  • Over-adjustment with non-EMMs can compromise the precision of transported effect estimates.
  • Understanding covariate roles is key to balancing validity and precision in epidemiologic research.