Triangulation in aetiological epidemiology: Approaches to causal inference
- Core Principle: This article formally advocates for Triangulation in aetiological epidemiology, defined as the integration of results from multiple research approaches where each approach possesses uncorrelated sources of bias.
- Causal Inference: If different methods (e.g., observational studies, Mendelian Randomization, and clinical trials) all converge on the same causal conclusion, confidence in that finding is significantly strengthened, especially when method-specific biases would predict opposing results.
- Addressing Inconsistency: When results are inconsistent, the framework helps identify which source of bias (e.g., confounding, reverse causation) is most likely distorting a given approach, thereby guiding the direction of future, more focused research.
PubMed: 28108528 DOI: 10.1093/ije/dyw314 Overview generated by: Gemini 2.5 Flash, 26/11/2025
Key Findings: Enhancing Causal Inference Through Triangulation
This foundational article in aetiological epidemiology advocates for the systematic use of Triangulation—the practice of integrating results from multiple distinct research approaches—to strengthen causal inference in the study of disease aetiology. The authors argue that relying on single methods or studies is inherently problematic due to the limitations and biases specific to that approach.
The Principle of Triangulation
Triangulation requires the simultaneous use of several approaches where:
- Different Bias Sources: Each approach must possess different key sources of potential bias that are uncorrelated with the biases of the other approaches. This ensures that any consistent finding is less likely to be an artefact of a single, shared flaw.
- Increased Confidence: When the findings from different, methodologically distinct approaches all point to the same conclusion regarding a causal relationship (e.g., exposure \(A\) causes outcome \(B\)), confidence in that causal finding is significantly increased.
Identifying and Addressing Bias
The power of triangulation is particularly evident when methodological biases are explicitly considered:
- Bias Prediction: The approach is strongest when the key sources of bias of some methods (e.g., unmeasured confounding in observational studies, reverse causation in cross-sectional studies) would predict findings that point in opposite directions if those biases were solely responsible for the observed association. Consistency despite these opposing biases provides strong evidence for causality.
- Inconsistency as a Guide: When inconsistencies or contradictions arise between the results of different approaches, the triangulation framework provides a mechanism to identify and dissect the key sources of bias inherent in each method. This process then guides researchers to design further, more robust studies to address the causal question.
Application in Aetiological Epidemiology
The paper illustrates the application of triangulation by combining evidence from a variety of sources to address epidemiological causal questions, including:
- Conventional Epidemiology: Standard cohort or case-control studies (prone to confounding and reverse causation).
- Mendelian Randomization (MR): Genetic association studies that use genetic variants as instrumental variables (less prone to confounding and reverse causation).
- Quasi-experimental designs: Such as sibling comparisons or natural experiments.
- Randomized Controlled Trials (RCTs): The gold standard for causality, often unfeasible or unethical for long-term aetiological questions.
By integrating evidence across these different methods, triangulation provides a robust framework for overcoming the inherent limitations of any single study design in defining causality.