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  Development of methods to triangulate multiple sources of evidence.

   Bristol Medical School

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project


Triangulation, in which multiple methods are strategically used to answer a single question, is a currently developing area. Lawlor, Tilling and Davey Smith (2016) explained how causal inferences can be strengthened by integrating results from several approaches with different key sources of potential bias. The statistical methods for combining the results from multiple sources of evidence within a triangulation framework are, however, underdeveloped. This PhD seeks to develop, illustrate and evaluate such methods.

Aims & Objectives

The project seeks to develop and implement quantitative methods for triangulation of multiple lines of evidence addressing the same underlying epidemiological question


Work is expected to focus on three key areas as follows.

1) At its simplest, triangulation involves comparison and combination of studies of the same exposure-outcome effect that use different designs or analytic methods. For example, randomized trials, Mendelian randomization studies and traditional multivariable regression analyses of observational evidence might all tackle a question relating to the same exposure-outcome effect. The studies may produce different effect estimates because they are (i) asking subtly different questions (e.g. in relation to the period or patterns of exposure), (ii) compromised by different biases and/or (iii) subject to chance. Triangulation combines these issues in a statistical model and assesses the extent to which the observed data fit together – an approach known as multiparameter evidence synthesis. Methods for producing these models, assessing coherence and drawing conclusions about causal effects of the exposure on the outcome will be developed. The project will primarily explore Bayesian methods, because they are flexible and allow incorporation of external information through prior distributions.

2) Another form of triangulation arises when some (or all) studies address only a component of the underlying question. For example, if the exposure-outcome effect occurs through an intermediate, then studies of the exposure-outcome effect might be triangulated with a combination of studies (i) of the effect of exposure on the intermediate and (ii) of the effect of the intermediate on the outcome. Methods will be developed to synthesise these three sets of studies, and account for true differences, biases and chance.

3) In addition to working on novel statistical methods, the student may explore other methodological questions. First, how should we define and identify studies suitable for a triangulation exercise? Automation tools may help here, such as MELODI (, which we have developed to identify studies examining intermediates between exposure and outcome. Second, how should we evaluate the risk of bias in studies for which formal frameworks (such as RoB 2 and ROBINS-I; have not been developed? Third, what sources of information are available about biases, to inform prior distributions, and how can more information be generated?

Methods developed in these three areas will be illustrated through application to important causal questions in epidemiology.

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.

If you have secured your own sponsorship or can self-fund this PhD please visit our information page here for further information on the department of Population Health Science and how to apply.

Biological Sciences (4) Mathematics (25) Medicine (26)


Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016 Dec 1;45(6):1866-1886. doi: 10.1093/ije/dyw314.
Munafò MR, Davey Smith G. Robust research needs many lines of evidence. Nature. 2018 Jan;553(7689):399-401. doi: 10.1038/d41586-018-01023-3.
Munafò MR, Higgins JPT, Davey Smith G. Triangulating Evidence through the Inclusion of Genetically Informed Designs. Cold Spring Harb Perspect Med. 2021 Aug 2;11(8):a040659. doi: 10.1101/cshperspect.a040659.
Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47. doi: 10.1111/j.1467-985X.2008.00547.x.
Ades AE, Welton NJ, Caldwell D, Price M, Goubar A, Lu G. Multiparameter evidence synthesis in epidemiology and medical decision-making. J Health Serv Res Policy. 2008 Oct;13 Suppl 3:12-22. doi: 10.1258/jhsrp.2008.008020.

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