Roadmap for systematic identification and analysis of multiple biases in causal inference

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Observational studies investigating causal effects depend on assumptions that, if violated, can introduce bias.
• Quantitative bias analysis (QBA) methods assess the sensitivity of findings to assumption violations.
• Current guidance on QBA is limited regarding systematic identification of underlying assumptions and potential violations.

Purpose of the Study:

• To propose a systematic roadmap for identifying and analyzing multiple biases in observational studies.
• To address the gap in guidance for determining assumptions and potential violations that should inform bias analysis.
• To enable more comprehensive and less selective QBA.

Main Methods:

• Articulate primary analysis assumptions by specifying the 'ideal trial' and its emulation requirements, visualized with causal diagrams.
• Extend causal diagrams to depict alternative assumptions that may lead to biases.
• Derive a single estimate that simultaneously corrects for all identified potential biases.

Main Results:

• The proposed roadmap systematically identifies assumptions and potential biases.
• Illustrative example using breastfeeding's effect on childhood asthma risk.
• Simulations demonstrate the necessity of joint analysis of multiple biases.

Conclusions:

• The developed roadmap provides a structured approach to bias analysis in observational studies.
• Systematic identification and joint analysis of multiple biases enhance the validity of causal effect estimates.
• This method improves the reliability of findings from observational research.