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Development of methods to triangulate multiple sources of evidence

  • Full or part time
  • Application Deadline
    Monday, November 25, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

The University of Bristol is offering a 3.5 year full time PhD in research around Population Health to start in 2019. This studentship is funded through GW4 BioMed MRC Doctoral Training Partnership. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£15,009 p.a. for 2019/20, updated each year). Additional research training and support funding of up to £5,000 per annum is also available.

Additional research and training funding is available over the course of the programme. This will cover costs such as research consumables, courses, conferences and travel. Additional competitive funds are available for high-cost training/research. The studentship is based at the Bristol Medical School. For further information please see the website below.

This is cross-disciplinary project that provides a unique opportunity at the interface of evidence synthesis, medical statistics and epidemiology. You will be supervised by Prof Julian Higgins (Univ. Bristol), Prof Stuart Logan (Univ. Exeter), Dr Alexandra McAleenan (Univ. Bristol) and Prof Kate Tilling (Univ. Bristol).


Triangulation, in which multiple methods are strategically used to answer a single question, is a currently developing area. Lawlor, Tilling and Davey Smith ( 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 in three key areas.

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. Quantitative 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?

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


The student will be attached to the MRC Integrative Epidemiology Unit (IEU), based in the Department of Population Health Sciences at Bristol Medical School, and will take regular visits to Prof Logan at the University of Exeter. The MRC-IEU provides a dynamic and supportive environment for training and development for all career stages in a world-leading multidisciplinary, collaborative research unit. We have 60 current PhD students within the IEU, while the wider department hosts several hundred more. Our focus at MRC IEU is on delivering research and teaching rooted in highly rigorous statistical appraisal of evidence in population health, with emphasis on causal methodology, statistical analysis, multi-omic data and advanced computing approaches, aligned with the MRC’s focus on areas of unmet national need in quantitative and interdisciplinary skills. Externally we are viewed as a beacon of training in innovative epidemiological methods and their application, such as Mendelian randomization, epigenetics and advanced statistical methods, attracting national and international applications for our PhD and short course programmes (

Candidate requirements:

Applications are welcome from high performing individuals with strong quantitative background such as in statistics or mathematics. Candidates are expected to have, or expected to obtain, a 2.1 or higher degree. Applications are particularly welcome from individuals with a Masters degree in medical statistics/biostatistics.

How to apply:

Please make an online application for this project here
Contact: Julian Higgins .
Closing date: 5pm, 25th November 2019

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