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Indirect effects in mediation analyses should not be tested for statistical significance
1Department of Epidemiology and Data Science, University Medical Centres, Amsterdam, The Netherlands.
Journal of Clinical Epidemiology
|May 16, 2024
Summary
Mediation analysis reveals issues with indirect effect standard errors, particularly when exposure-mediator and mediator-outcome relationships are equally strong. Clinical relevance, not statistical significance, should guide the evaluation of indirect effects.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Mediation analysis dissects exposure-outcome effects into direct and indirect components.
- A common issue in mediation analysis is the underestimation of the indirect effect's standard error compared to direct and total effects.
- This standard error problem is often overlooked in epidemiological research.
Purpose of the Study:
- To highlight the problem of standard error estimation for indirect effects in mediation analysis.
- To offer guidance on statistically testing indirect effects.
Main Methods:
- Utilized two real-world datasets for illustration.
- Conducted several simulations to investigate the standard error estimation problem.
Main Results:
- The standard error estimation issue is most significant when exposure-mediator and mediator-outcome relationships are of comparable strength.
- The magnitude of this estimation problem varies depending on the strength of the mediation effect.
Conclusions:
- Statistical significance testing for indirect effects in mediation analysis is discouraged.
- The clinical relevance of mediation should be the primary consideration for evaluation.
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