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PreprintCited by 125Advanced Causal Inference TechniquesGenetic Associations and EpidemiologyStatistical Methods in Epidemiology

Triangulating Evidence through the Inclusion of Genetically Informed Designs

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Munafò, Marcus R., Higgins, Julian P.T., Smith, George Davey·2021·Cold Spring Harbor Perspectives in Medicine
DOI 10.1101/cshperspect.a040659PMC PMC8327826
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Abstract

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

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  • Causal effect estimates from MR can be evaluated within a triangulation of evidence framework, which involves interpreting findings alongside results from complementary approaches that rely on different assumptions.

    “Causal effect estimates from MR can be evaluated within a triangulation of evidence framework, which involves interpreting findings alongside results from complementary approaches that rely on different assumptions.”

    From Mendelian randomization (2022) · cites this paper as [6]

  • Triangulation of evidence from multiple methodologies subject to different sources and directions of bias can help identify bias in MR studies, and alignment of results across methodologies can improve confidence in an initial causal interpretation.

    “Triangulation of evidence from multiple methodologies — using different methodologies that are subject to different sources and directions of potential bias — can help to identify bias in MR studies.6,7,155 Alignment of results across these different methodologies can improve confidence in an initial causal interpretation.”

    From Mendelian randomization (2022) · cites this paper as [6]

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1 of the 86 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Becker BJ. 1994. Combining significance levels. In The hand- book of research synthesis (ed. Cooper H, Hedges LV), pp. 215-230.
  2. Weisberg HI. 2014. Wilful ignorance: the mismeasure of un- certainty. Wiley, Hoboken, NJ.

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Authors
Munafò, M. R., Higgins, J. P. T., & Smith, G. D.
Affiliations
University Hospitals Bristol NHS Foundation Trust (GB)
ORCID
Venue
Cold Spring Harbor Perspectives in Medicine
Publisher
Cold Spring Harbor Laboratory
Published
2020-12-21
Volume
11
Issue
8
Pages
a040659
ISSN
2157-1422 (ISSN-L)
DOI
10.1101/cshperspect.a040659
PMC
PMC8327826
OpenAlex
W3113742933
Cited by
125
References
86
Citations by year
2026: 17 · 2025: 15 · 2024: 62 · 2023: 10 · 2022: 12 · 2021: 8 · 2020: 1
Type
posted content
Language
en
Open access
Locations
  • Cold Spring Harbor Perspectives in Medicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (submittedVersion)
Funders
  • National Institute for Health and Care Research (NF-SI-0617-10145)
  • University of Bristol (MC_UU_00011/7, MC_UU_00011/1)
  • Medical Research Council (MC_UU_00011/7, MC_UU_00011/1, MC_UU_00011)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
genetically informed designs, causal inference, residual confounding, reverse causality, genetic confounding, environmental confounding, triangulation of evidence, bias in observational studies, disease onset and progression
MeSH terms
Disease, Genetics, Research Design, Causality

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