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Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
Anita Lindmark1, Xavier de Luna1, Marie Eriksson1
1Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, 90187, Sweden.
Statistics in Medicine
|February 21, 2018
Summary
This study introduces a new sensitivity analysis for mediation analysis with binary data. It quantifies how unmeasured confounding affects estimates of direct and indirect effects.
Area of Science:
- Epidemiology
- Biostatistics
- Causal Inference
Background:
- Estimating direct and indirect effects in mediation analysis requires untestable unconfoundedness assumptions.
- Sensitivity analysis is crucial to assess the robustness of mediation estimates to potential unmeasured confounding.
Purpose of the Study:
- To propose a novel sensitivity analysis method for parametric mediation analysis with binary exposure, mediator, and outcome.
- To quantify the impact of unmeasured confounding on direct and indirect effect estimates.
Main Methods:
- The method uses correlations between error terms of exposure, mediator, and outcome models as sensitivity parameters.
- Parametric estimation incorporates these correlations to derive identification sets for direct and indirect effects.
- Sampling variability is addressed using uncertainty intervals.
Main Results:
- The proposed method assesses sensitivity to both mediator-outcome and exposure-related confounding.
- Identification sets provide a range of plausible effect estimates under varying confounding levels.
- The approach was illustrated using data from the Swedish Stroke Register (Riksstroke).
Conclusions:
- The developed sensitivity analysis enhances the reliability of mediation findings in the presence of potential unmeasured confounding.
- This method provides a quantitative tool for researchers to evaluate the plausibility of their mediation effect estimates.
- An R package is available to facilitate the application of this sensitivity analysis technique.
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