Outline

Antimicrobial Resistance (AMR) is one of the major health concerns of our time. The development and transmission of microbials which do not respond to treatment has led to the need to develop and evaluation interventions which aim to promote a judicious use of antimicrobials (termed “antimicrobial stewardship” (AMS)) and also the need to measure outcomes pertaining to antimicrobial use, clinical patient-reported outcomes, and microbiological outcomes.

Laboratory analyses of biological samples typically produce complex, high-dimensional datasets which are often severely reduced in simplicity during trial analysis and reporting. This data reduction leads to a loss of information and ultimately leads to a lack of high-quality informative microbiological evidence for or against AMS interventions.

The aim of this PhD studentship is to investigate the use of modern statistical modelling methods for the analysis of microbiological data arising from clinical trial research.

The student will begin by reviewing the literature to gain a comprehensive overview of analytical methods used in clinical trials which collect microbiological data. The student will investigate the use of statistical methods under the following scenarios:

• Modelling cross-sectional microbiology data;

• Modelling longitudinal microbiology data;

• Modelling microbiological mechanisms in clinical trials.

The student will work closely with a stakeholder group of clinicians, microbiologists, and statisticians to identify key microbiological data components which should inform decisions regarding AMS interventions, and from this identify methods which may adequately model the required complexity and produce findings which can be communicated to clinical and microbiological audiences effectively.

The student will have access to data from completed clinical trials and observational data of common infections across of range of ages, conditions, settings, and including a variety of biological samples.

Key outputs from this work will include guidance on the analysis of microbiological data in clinical trials – with accompanying workshop to effectively disseminate the findings and maximise implementation of the methods.

As part of the PhD, the student will be aligned with the Statistical Analysis Working Group of the MRC NIHR Trial Methodology Research Partnership (TMRP).

What is funded

This is a competitive process, with four projects advertised and only one studentship will be funded.The studentship is generously funded by the School of Medicine as part of the MRC NIHR Trials Methodology Research Partnership (TMRP).

Open to all UK/EU students without further restrictions

Full UK/EU tuition fees

Doctoral stipend matching UK Research Council National Minimum.

Additional funding is available over the course of the programme and will cover costs such as research consumables and training.

Applications from International candidates are welcomed if they can cover the difference in home/Eu fees (£4,407) and Overseas fees (£21,950).

Eligibility

Applicants should possess a minimum of an upper second class Honours degree or master's degree, or equivalent in Statistics or with a substantial statistical component.

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS)

How to Apply

This studentship has a start date of October 2020. In order to be considered you must submit a formal application via Cardiff University’s online application service. (To access the system click 'Apply Online' at the bottom of this advert)

There is a box at the top right of the page labelled ‘Apply’, please ensure you select the correct ‘Qualification’ (Doctor of Philosophy), the correct ‘Mode of Study’ (Full Time) and the correct ‘Start Date’ (October 2020). This will take you to the application portal.

In order to be considered candidates must submit the following information:

• Supporting statement

• CV

• Qualification certificates

• References x 2

• Proof of English language (if applicable)

In the 'Research proposal and Funding' section of your application, please specify the project titles and supervisors of the project/s.

Please Note* the supervisor reserves the right to close the advert early if sufficient applications are received.