PhD opportunity at UEA: NETWORK META-ANALYSIS OF DIAGNOSTIC ACCURACY STUDIES VIA FACTOR COPULA MIXED MODELS

Dear Grstats,

I am pleased to announce a PhD opportunity at the University of East Anglia that is open for applications. The PhD project is entitled “Network meta-analysis of diagnostic accuracy studies via factor copula mixed models”.

Further details are outlined below.

Best wishes,

Aris

Dr Aristidis K. Nikoloulopoulos | Associate Professor in Statistics |  School of Computing Sciences
Room 2.11A, Science Building, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ
Email: a.nikoloulopoulos@uea.ac.uk   | Web:  https://research-portal.uea.ac.uk/en/persons/aristidis-k-nikoloulopoulos

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APPLICATION DEADLINE

28 November 2021

LOCATION

University of East Anglia

START DATE

1st October 2022

SUPERVISOR

Dr Aristidis K. Nikoloulopoulos (https://research-portal.uea.ac.uk/en/persons/aristidis-k-nikoloulopoulos)

PROJECT OVERVIEW

The identification of the most accurate diagnostic test for a particular disease contributes to the prevention of unnecessary risks to patients and healthcare costs. Clinical and policy decisions are usually made on the basis of the results from many diagnostic test accuracy studies on the same research question. The considerably large number of diagnostic test accuracy studies has led to the use of meta-analysis. The purpose of a meta-analysis of diagnostic test accuracy studies is to combine information over different studies and provide an integrated analysis that will have more statistical power to detect an accurate diagnostic test than an analysis based on a single study. As the accuracy of a diagnostic test is commonly measured by a pair of indices such as sensitivity and specificity, the synthesis of diagnostic test accuracy studies is the most common medical application of multivariate meta-analysis. Most of the existing meta-analysis models and methods have mainly focused on a single test. As the meta-analysis of more than one diagnostic test can impact clinical decision-making and patient health, there is an increasing body of research in models and methods for meta-analysis of studies comparing multiple diagnostic tests. The application of the existing models to compare the accuracy of three or more tests suffers from the curse of multi-dimensionality. To overcome these issues in network meta-analysis of studies comparing multiple diagnostic tests, we will study parsimonious copula mixed models for comparing multiple diagnostic tests that can incorporate studies with different designs and studies with or without a gold standard. For the between-studies model, we will exploit the use of factor copula distributions. Factor copulas can provide a wide range of dependence and allow for different types of tail behaviour, different from assuming simple linear correlation structures, normality and tail independence.


REFERENCES

Nikoloulopoulos, A. K. (2015). A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution. Statistics in Medicine, 34:3842–3865.  

Nikoloulopoulos, A. K. (2019). A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests. Statistical Methods in Medical Research, 28(10-11):3286–3300.

Nikoloulopoulos, A. K. (2020a). A multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic studies in the presence of non-evaluable subjects. Statistical Methods in Medical Research, 29(10):2988–3005.

Nikoloulopoulos, A. K. (2020b). A multinomial 1-truncated D-vine copula mixed model for meta-analysis and comparison of multiple diagnostic tests. ArXiv e-prints. arXiv:2010.08152.

Nikoloulopoulos, A. K. (2020c). An one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests.  ArXiv e-prints. arXiv:2006.09278.


ENTRY REQUIREMENTS

Acceptable first degree: Mathematics, Statistics, Actuarial Science. The project requires a 1st.

FUNDING

This PhD project is in a competition for a Faculty of Science funded studentship.  Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend of £15,609 for 3 years.  Applicants who are not eligible for home tuition fees are welcome to apply but they will be required to fund the difference between home and international tuition fees, which you can find details for 2021-22 on the University’s feespages. Please note tuition fees are subject to an annual increase.

APPLICATION PROCESS

Full details and the application process can be found at https://www.uea.ac.uk/course/phd-doctorate/network-meta-analysis-of-diagnostic-accuracy-studies-via-factor-copula-mixed-models-nikoloulopoulosa-u22scio. For more information on the project, please contact Dr Aristidis K. Nikoloulopoulos (Email: a.nikoloulopoulos@uea.ac.uk)