Summary
This methodological paper presents triangulation as a framework for strengthening causal inference in aetiological epidemiology by integrating results from multiple study approaches with independent bias structures. The authors propose minimum criteria for triangulation, systematise the key sources of bias in common epidemiological approaches, and emphasise the importance of explicitly predicting bias direction and seeking approaches that would bias estimates in opposite directions. Three worked examples illustrate how inconsistencies or convergence across methods can guide causal interpretation and identify directions for future research.
UK applicability
This methodological framework is directly applicable to UK epidemiological research and evidence synthesis, offering a structured approach for strengthening causal inference in food, nutrition, and health policy guidance. It is particularly relevant to UK health research institutions and bodies conducting systematic evidence reviews for public health recommendations.
Key measures
Methodological framework criteria for triangulation; characterisation of bias direction across study approaches; consistency of causal conclusions across multiple methods
Outcomes reported
The paper illustrates triangulation as a methodological approach to improve causal inference by integrating results from multiple epidemiological study designs, each with different sources of bias. It proposes criteria for triangulation and demonstrates the framework through three worked examples.
Topic tags
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