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Current studentships

Building a risk prediction model for Endometrial Cancer

MRC Funded PhD Studentship, please see below

Project Description

Endometrial cancer (EC) is the most common gynaecological malignancy in the UK with 8,984 women diagnosed and 2,360 deaths each year. Over the past three decades, EC mortality rates have been increasing year on year and are projected to rise by a further 19% by 2035. For women with advanced disease, 5-year survival is around 14%.

Obesity is the biggest known risk factor for EC. A global increase in obesity, along with prolonged life expectancy, is likely to lead to increases in EC rates of at least 50% by 2030 in high-income countries.  There is an urgent need to identify women at risk based on epidemiological and genetic factors and identify biomarkers that could be used for risk stratification so that preventative strategies such as progestin-based hormonal treatments or screening could be offered appropriately.

This studentship will include:

A literature review to identify EC risk factors and serum biomarkers Evaluation of epidemiological risk factors associated with EC using the large cohort of 202,638 postmenopausal women taking part in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS)

 

The primary supervisor will be Dr Aleksandra Gentry-Maharaj, along with secondary supervisors, Dr Matthew Burnell and Professor Usha Menon. A tertiary supervisor, Dr Anne Dawnay, will oversee the biomarker studies.

  • Identification of serum biomarkers associated with EC risk in a UKCTOCS nested case-control study
  • Develop and run a Genome Wide Association Study (GWAS) to identify SNPs that increase/decrease risk of EC in a nested case-control study using the EC samples collected in UKCTOCS
  • Build a risk prediction model based on lifestyle factors, serum biomarkers and GWAS SNPs to predict risk of (1) developing EC and (2) developing fatal EC

Applications including a CV, personal statement, and the contact details of two referees, should be emailed to ictm.education@ucl.ac.uk.  

If you would like to have an informal discussion about this PhD opportunity then please contact Dr Aleksandra Gentry-Maharaj

Deadline for applications is Friday 29th November 2019

Person Specification

This project would suit a candidate with a background in epidemiology and a good grasp of relevant statistics; however, extensive statistical experience is not essential, as the student will receive support from the UKCTOCS senior statistician. The Unit has a reputation of similar PhD projects being successfully completed in the past.

How to Apply

Applications including a CV, personal statement, and the contact details of two referees, should be emailed to ictm.education@ucl.ac.uk.  

If you would like to have an informal discussion about this PhD opportunity then please contact Dr Aleksandra Gentry-Maharaj

Deadline for applications is Friday 29th November 2019

Developing and implementing multi-arm, multi-stage adaptive randomised controlled trials

MRC Funded PhD Studentship, please see below for further information including how to apply:

Project Description

Randomised controlled trials (RCTs) are the gold-standard for testing whether a new treatment is better than the current standard of care. But traditional designs take a long time and are often expensive, increasingly so in both cases.

To overcome these issues, the multi-arm, multi-stage (MAMS) trial design has been proposed, which tests several different treatments in parallel and ceases recruitment early to those treatments which appear futile using a staged approach [1]. The advantages over traditional designs are as follows: 1) it saves time compared to testing all the treatments sequentially in a series of two-arm trials; 2) It increases the probability of identifying a better treatment; 3) It allows testing of more treatments than would ever be performed in two-arm trials; 4) It is considerably cheaper and more efficient than comparable two-arm trials.

The MAMS approach is one of the few adaptive designs being deployed in a number of trials and across a range of diseases, including ROSSINI-II, RAMPART, STAMPEDE, CompARE, TRUNCATE-TB, prompting changes to treatment guidelines in patients with prostate cancer in the case of STAMPEDE trial. The main aim of this PhD project would be to extend the design, to explore its statistical properties, and to develop some practical guidance on implementation of the methods for practitioners.

MRC Clinical Trials Unit at UCL is leading the way in the design and implementation of the MAMS approach to trial design. We have the ideal level of expertise, practical experience and data to be able expand the applicability of the design, and also to construct formal guidance on their design and analysis.

The lead supervisors will be Dr Babak Oskooei and Prof. Max Parmar at the MRC Clinical Trials Unit (CTU) at UCL. A supervisory team will be established, including other experts from within the MRC CTU at UCL with strong practical experience in the design and conduct of MAMS trials.

Person Specification 

This project would suit a candidate with strong statistical and computational skills, and the ability to work independently at times. Ideally, the candidate would be highly numerate with a strength for developing statistical methodology and an enthusiasm for applying those methods into practice, e.g. a Master’s degree or equivalent in medical statistics or a related quantitative field. Some experience of working on clinical trials may be helpful.

How to apply

Applications must only include CV, personal statement, and the contact details of two referees, and should be emailed to ictm.education@ucl.ac.uk.  

If you would like to have an informal chat / discussion about this PhD opportunity then please contact Dr Babak Choodari-Oskooei, b.choodari-oskooei@ucl.ac.uk

Deadline for applications is Friday 29th November 2019


PhD Studentships in Medical Statistics and/or Clinical Trials Methodology. Tuition fees and stipend starting from at least £16,553 per annum tax-free

Applications now open

Avaliable Projects

The following projects are available:

Statistical methods for adjusting for treatment changes in randomised trials One-stage models for meta-analysis Exploring the implications of analysing time-to-event outcomes as binary in meta-analysis

These projects will all allow the student to develop skills in the analysis of data in medical research, and in the methodology of clinical trials. Brief details of each project are provided at the end of this advert and further details of each are available from Dr Claire Vale, who is also very happy to discuss projects with potential applicants, and to answer any queries about studying at the Institute.

You will join the group of PhD students within the ICTM at UCL. You will register for full-time MPhil/PhD study at UCL, and will benefit from the range of training provided through UCL.

Design and analysis of trials with multiple primary outcomes Methodology to maximise the potential of  evaluating many therapies within a single protocol and developing Living Trial Protocols Improving assessment and monitoring of neurocognitive function in clinical studies involving HIV+ participants Another project in methodology for clinical trials to be negotiated with potential supervisors at the unit placeholder

Person Specification

Essential

Have (or will have completed completed before starting your study with us) an MSc in a field relevant to the preferred research project, or equivalent experience together with a BSc in a quantitative discipline. Experience in statistical programming and use of statistical software such as Stata, to the level required by the preferred research project Good communication skills Interest in preferred research project, with some understanding of the issues it addresses Understanding of the principles of clinical trials, medical statistics, research methods, epidemiology and meta-analysis if relevant to the preferred project

 

Under research council regulations all candidates are also required to have been resident in the UK for the past three years, and thereby qualify for home fees status. 

More details on eligibility can be found on the MRC website: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/

Desirable

Work experience in statistics or medical research Publication record appropriate to work experience

Clear articulation of the reasons to wish to undertake a PhD Ability to work independently and manage own time

Description of Proposed Projects

More detailed descriptions of each project and the supervisors involved are available from Dr Claire Vale, and candidates are strongly encouraged to obtain these detailed descriptions before making their application. Dr Claire Vale will also send details of any further projects developed since this advert was prepared.

Statistical methods for adjusting for treatment changes in randomised trials

http://www.ctu.mrc.ac.uk/our_research/research_types/methodology1/design/ http://www.ctu.mrc.ac.uk/our_research/research_types/methodology1/analysis/ http://www.ctu.mrc.ac.uk/our_research/research_types/methodology1/conduct/

We also welcome expressions of interest to study projects related to our priority research areas, including:

Anti-retroviral therapy has proved to effectively suppress viral replication and halt disease progression in patients living with HIV. However, effectively suppressed patients seem to be at higher risk for specific end-organ disease, including cognitive impairment, which have become important endpoints for HIV treatment clinical trials. Assessing and monitoring cognitive function, in both clinical practice and research studies, is mostly based on neuropsychological testing performance and different scales and criteria have been proposed to define impairment. Large and well powered randomised and observational studies have prospectively investigated cognition in effectively suppressed, treatment naïve and vertically infected individuals. This project aims to further improve understanding on how cognitive function is being measured in HIV treatment trials and cohorts and to provide evidence-based recommendations for the analysis and interpretation of neuropsychological longitudinal data.

Another project in statistical methodology for clinical trials to be negotiated with potential supervisors at the unit

When faced with the need to evaluate a new therapy, we can either design set up and run a new trial, or we can seek to graft the new comparison onto an existing trial, by making appropriate adaptions. The latter approach gives rise to trials with ‘living protocols’ which adapt over time. Within the unit, the STAMPEDE trial in prostate cancer has a ‘living protocol’: it started out with five arms, and additional arms have been added, both from results of the STAMPEDE trial and from other trials. The purpose of this PhD is to address the methodological issues arising from living protocol trials, starting with a framework for control of the type one error and appropriate primary analysis. The goal is to provide guidance on when living protocols are appropriate, and how they should be designed and analysed.

Improving assessment and monitoring of neurocognitive function in clinical studies involving HIV+ participants

Trials sometimes assess interventions that are expected to improve two or more aspects of health, where the impact may vary across these aspects. The project will draw on example trials including (i) a trial of a contraception choice intervention in the UK hoping to increase use of a highly effective contraceptive method, or satisfaction with current method, or both, and (ii) a trial of an intervention in Kenya hoped to improve (some or all of) antenatal clinic attendance, delivery at a health centre, postnatal clinic attendance and infant vaccination. Specifying just one primary outcome for such a trial is illogical and risks drawing the wrong conclusion - missing the important benefit of an intervention on other outcomes or declaring an intervention a success when it is beneficial for the primary outcome but harmful for others. Handling multiple primary outcomes however is challenging. This project aims to review and offer guidance on the best ways of analysing and interpreting trial data to decide whether an intervention can be recommended, and with this in mind also guide how to calculate the sample size for trials. 

Methodology to maximise the potential of evaluating many therapies within a single protocol and developing Living Trial Protocols

A meta-analysis combines evidence from a set of similar studies, using data extracted for a suitable effect measure. When combining data from trials evaluating time-to-event outcomes such as overall survival, the most appropriate effect measure is the hazard ratio. However, time-to-event outcomes are commonly dichotomised and analysed as binary outcomes in meta-analysis, using effect measures such as the risk ratio or odds ratio. This project is focused on exploring the implications of analysing time-to-event outcomes as binary in meta-analysis. Investigations will include large-scale analysis of a collection of published meta-analyses extracted from Cochrane reviews, detailed exploration of case study meta-analyses held by the MRC CTU at UCL, and simulation studies. The aim of this project is to provide information and guidance to researchers carrying out meta-analyses of time-to-event outcomes.

Design and analysis of trials with multiple primary outcomes

Modern medicine aims to be based on evidence, and the highest quality evidence comes from systematic review of randomised trials. At the heart of systematic review is meta-analysis, the statistical method for combination of evidence about treatment effects from multiple randomised trials. Meta-analysis is usually done in a two-stage process: first estimate treatment effects in all studies, then form a weighted average. However, two-stage meta-analysis is approximate and inaccurate in many settings, especially with binary outcome data. This project instead explores one-stage meta-analysis, where all the data are analysed in a single model. Various problems in frequentist one-stage meta-analysis have recently come to light. This project will explore these issues and ultimately aims to offer guidance on how these models should be fitted and to provide convenient software for doing so.

Exploring the implications of analysing time-to-event outcomes as binary in meta-analysis

Many clinical trials have substantial numbers of participants who change treatments during follow-up. Treatment changes can mask any benefit of treatment and in this case it is often important to adjust for the treatment changes in the statistical analysis. This is particularly an issue in late-stage cancer trials. This project will develop appropriate statistical methods, drawing on causal inference methods, with particular emphasis on disseminating methods for example by producing user-friendly software.

How to Apply

The application requires:

Your CV including contact details of two referees, and a letter explaining why you wish to study for a PhD in the ICTM at UCL and how you meet the essential and desirable criteria listed above.

For shortlisted candidates there will be an opportunity to discuss the nature of each project at interview. 

Please email your CV and letter of interest to ictm.education@ucl.ac.uk.

In your letter you may express a preference for one of the above listed projects, and briefly explain your preference. If you wish alternatively to propose your own project then details of the project should be provided with your letter. Candidates intending to propose their own project are encouraged to contact Dr Claire Vale in advance of applying to discuss the suitability of the project and potential supervisors.

Please note that successful applicants will also be required to apply for MPhil/PhD admission via the UCL online application system.

More information