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Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to
Kosuke Inoue1,2, Kentaro Sakamaki3, Sho Komukai4
1Department of Social Epidemiology, Graduate School of Medicine, Kyoto University.
Journal of Epidemiology
|July 7, 2024
Summary
Accurate observational studies require careful confounder selection to avoid bias. This study reviews methods for identifying confounders and assessing sensitivity to unmeasured factors, improving causal effect estimation.
Area of Science:
- Epidemiology
- Biostatistics
- Causal Inference
Background:
- Observational studies risk bias from confounding, even with large sample sizes.
- Accurate causal effect estimation relies on identifying and adjusting for confounders.
- Existing methods for confounder selection have limitations.
Purpose of the Study:
- To summarize epidemiological and statistical approaches for identifying sufficient confounder sets.
- To discuss pitfalls in confounder selection, including instrumental and intermediate variables.
- To introduce sensitivity analysis tools for unmeasured confounders.
Main Methods:
- Review of epidemiological and statistical confounder selection strategies.
- Introduction of the modified disjunctive cause criterion.
- Application of E-value and robustness value for sensitivity analysis.
Main Results:
- The modified disjunctive cause criterion offers a robust approach to confounder identification.
- Statistical methods aid confounder selection with numerous covariates, even in small studies.
- E-value and robustness value provide quantitative sensitivity assessments.
Conclusions:
- Effective confounder selection is vital for reliable observational study findings.
- Understanding and applying these methods enhances causal inference.
- Improved reporting and interpretation of epidemiological studies are facilitated.
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