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Sampling weighting strategies in causal mediation analysis.

Donna L Coffman1, Haoyu Zhou2, Katherine E Castellano3

  • 1Department of Psychology, University of South Carolina, 1512 Pendleton St., Columbia, 29208, USA. dcoffman@mailbox.sc.edu.

BMC Medical Research Methodology
|June 15, 2024
PubMed
Summary

Incorporating sampling weights in both stages of causal mediation analysis significantly reduces bias. This method is effective across various data structures and sampling techniques, improving causal mechanism examination.

Keywords:
Mediation analysisPropensity scoresSampling weights

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Area of Science:

  • Statistics
  • Epidemiology
  • Causal Inference

Background:

  • Causal mediation analysis is vital for understanding causal effects and mechanisms.
  • The integration of sampling weights into causal mediation analysis remains underexplored.
  • This study investigates various strategies for incorporating sampling weights.

Purpose of the Study:

  • To compare different methods of incorporating sampling weights into causal mediation analysis.
  • To evaluate the performance of these strategies in reducing bias.
  • To identify the most effective approach for weighted causal mediation analysis.

Main Methods:

  • A simulation study was conducted assessing four sampling weighting strategies.
  • Strategies included no weights, weights in mediation, weights in outcome models, and weights in both stages.
  • The study utilized 8 population scenarios and 4 sampling methods, totaling 32 simulation conditions, and applied strategies to the NSDUH dataset.

Main Results:

  • Weighting in both mediation and outcome models demonstrated the lowest bias across most simulations.
  • Employing sampling weights in only one stage resulted in increased bias under several conditions.
  • The findings highlight the impact of weighting strategy on the accuracy of mediation effect estimation.

Conclusions:

  • Incorporating sampling weights in both stages is a robust strategy for minimizing bias in causal mediation analyses.
  • This approach is effective across diverse population data structures and sampling methodologies.
  • The findings provide practical guidance for researchers conducting weighted causal mediation analyses.