This opportunity is now closed.

Funded PhD Opportunity

The impact of the analytical performance of laboratory tests on clinical decision making

Subjects: Computer Science and Informatics and Computer Science and Informatics


Summary

Research problem:

Medical laboratory test results are believed to inform 70% of clinical management decisions e.g. the formulation of a diagnosis, disease surveillance, identification of patients at risk of a disease, as well as, the decision to start, discontinue, or adjust a particular treatment. Furthermore, laboratory testing is a key element in 80% of all clinical practice guidelines. However, all laboratory test results are subject to analytical error arising from both analytical bias and imprecision which are intrinsic attributes of the analytical measurement system used.

Where laboratory test results are used for diagnosis, disease monitoring or risk stratification, analytical error may result in misdiagnosis or incorrect stratification of patient risk and inappropriate clinical management decisions. The impact of analytical error on clinical decision making and patient outcomes is not fully recognized by either clinicians, laboratory professionals or biomarker discovery scientists and the topic has received little attention in the biomedical literature. Defining the impact of the analytical performance of laboratory tests on clinical decision making will help inform the design of care pathways and the minimum analytical performance requirements for individual tests to allow optimum clinical utility.

Project rationale:

The analytical performance characteristics (expressed as bias and imprecision) are readily available for a large range of laboratory tests. For care pathways, which incorporate laboratory test cut offs for diagnostic or clinical management decisions, computer simulation modelling can be used to examine the clinical impact of variation in test imprecision and bias. Computer simulation offers many advantages. Firstly it is efficient and cost-effective. Secondly, such insights cannot be readily obtained in any other way since to perform clinical trials using tests of differing analytical performance characteristics would pose major ethical, logistical and trial design challenges and would be prohibitively expensive. Thirdly, unlike clinical trials, modelling poses no patient safety risks.

Project aim and objectives:

The aim of this project is to use simulation modelling based on anonymised patient data, derived from a medical laboratory database, to explore the relationship between analytical error for selected laboratory tests, clinical decision making, and potential patient impact. The project will consider whether simulation modelling of analytical error can be used to inform the minimum analytical test performance necessary to yield maximum clinical utility for individual medical laboratory tests.

The specific objectives of this study include:

1) For selected tests, develop a simulation approach to determine the effects of assay imprecision and bias on clinical decision making within relevant clinical care pathways and patient outcomes using real patient data extracted from a medical laboratory database.

2) For selected tests, use simulation modelling to define the minimum acceptable analytical performance requirements necessary to optimise clinical utility of the test.

3) Use modelling approaches to compare performance of clinical assays with performance criteria set by regulatory agencies.

Resources:

The PhD student will be supported by staff/RA’s/PhD students within CNET and Life and Health Science. Also available to the candidate are MATLAB/C++/Python/cluster computing and data access to laboratory datasets from the Altnagelvin Area Hospital


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Publications - peer-reviewed
  • Experience of presentation of research findings
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Friday 7 February 2020

Interview Date
23 to 24 March 2020


Applying

Apply Online  


Campus

Magee campus

Magee campus
A key player in the economy of the north west


Contact supervisor

Professor Liam Maguire


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