Triangulation in aetiological epidemiology

By Deborah Lawlor, K. Tilling, George Davey Smith. International Journal of Epidemiology, 2016

1,070 citations recorded by the source corpus.

Abstract Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.

Subfields: Genetics, Health, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/8ed819773b0e480d384cecb6fa7b55c5eb1c55de

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Citations in the corpus

Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “These data have previously been analysed and interpreted by Lawlor et al.7 and Bowden et al.4 They are included here for the purposes of illustration, rather than to draw any novel epidemiological conclusions.”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “These data have previously been analysed and interpreted by Lawlor et al. 7 and Bowden et al .”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “DOI: https://doi.org/10.7554/eLife.34408.006 independent studies and triangulation with evidence from other study designs ( Lawlor et al., 2016 ; Munafo` and Davey Smith, 2018 ).”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, background): “1 3 However, best practice would always be to triangulate across non-IV and MR approaches, and across multiple data sources wherever possible [66].”
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018, methodology): “[20], but we extend their original analysis here by applying our modified weights and conducting a more in depth inspection of each variant’s contribution to the overall heterogeneity.”
A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E) (2024, positive): “…together in a triangulation exercise, in which both the risks EPA A u t ho r M anu sc r i p t EPA A u t ho r M anu sc r i p t EPA A u t ho r M anu sc r i p t of bias (assessed by ROBINS-E) and differences between causal questions being addressed are considered jointly (Lawlor et al., 2016).”
A systematic map of research exploring the effect of greenspace on mental health (2020, background): “…about the circumstances under which causal inference can be tested and assumed (Lawlor, Tilling, & Smith, 2016; Munafò & Davey Smith, 2018; Pearce & Lawlor, 2016), the temporal perspective of longitudinal studies might enable researchers to identify points in time when exposure to the greenspace…”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “The next major transformation in MR is likely to involve the improvement of causal inference by incorporating information from beyond the SNP-exposure and SNP-outcome effects, in the spirit of triangulation of evidence (16,18).”
An atlas of genetic influences on osteoporosis in humans and mice (2018, neutral): “27 Our fracture GWAS identified 13 loci.”
Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, background): “Our analyses highlight that because these different causal inference techniques have varying strengths and weaknesses, triangulation of evidence from as many sources as possible should be practiced in causal inference [38].”