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Efficient ways of predicting recruitment in randomised controlled trials


About This PhD Project

Project Description

NDORMS 2019/6

Large multicentre randomised controlled trials are regarded as the gold standard for evaluating the effects of healthcare interventions. Participants are recruited across a number of centres and are randomly assigned to treatment groups. Research funders require researchers to calculate how many participants they require to address a specific research question, how long they need to recruit participants into the trial and how many trial sites are needed in order to reach that target.

Rates of participant recruitment are not always straightforward and can have an enormous impact on the planning, execution and funding of randomised trials. Recruitment is often slower and more difficult than expected with many trials failing to reach their planned sample size within the timescale and funding originally envisaged. Failure to recruit sufficient numbers of participants, or extended delays in recruitment can have serious implications for the success or failure of the trial.

It’s estimated that less than one third of publically funded trials recruit according to their original recruitment target, often resulting in requests for additional funding and/or time extensions and delaying the use of the results of the trial in clinical practice. Studies have shown that poor recruitment is by far the most frequent reason for early discontinuation of randomised trials. Current methods for predicting recruitment in randomised trials are limited and have limited clinical applicability. The recent James Lind Alliance priority setting partnership Prioritising recruitment in Randomised trials identified better ways of predicting recruitment in randomised trials as a key area for research.

The aims of this project are to provide a better understanding of what factors influence recruitment in large multicentre randomised trials and whether the use of simple, easy to use prediction tools, at the planning stage of a trial, can provide more accurate and realistic estimates of participant recruitment.

Details of Research Group:


The DPhil will be jointly supervised by Associate Prof Sally Hopewell and Prof Gary Collins, based at the Centre for Statistics in Medicine (CSM) and the Oxford Clinical Trials Research Unit (OCTRU), NDORMS, University of Oxford.

• Associate Prof Sally Hopewell’s research interests are focused on the design, conduct and transparent reporting of randomised trials and systematic reviews and has published extensively in this area. She is co-chief investigator on a large multicentre NIHR funded trial of exercise interventions for people with rotator cuff disorders.

• Dr Joanna Moschandreas is a senior medical statistician whose responsibilities include providing advice on trial design, both within the trials unit and as an NIHR Research Design Service adviser. She is currently the senior statistician on eight trials. Her research interests include identifying appropriate methods to maximise the available information in trials.

• Prof Gary Collins’ research interests are focused on methodological aspects surrounding the development and validation of multivariable prediction models and has published extensively in this area. He has a particular research focus on the novel application of prediction models in clinical trials

Training:


The Centre for Statistics in Medicine (www.csm.ox.ac.uk/) and Oxford Clinical Trials Research Unit (www.octru.ox.ac.uk/) are located in the Botnar Research Centre. CSM has more than 20 years’ experience in medical statistics and clinical trials. OCTRU, part of the CSM, is a fully registered Clinical Trials Unit, involved in the design, conduct and report in both early and later phase clinical trials in surgery, musculoskeletal sciences, respiratory medicine, gastroenterology and oncology.

Training will be provided in relevant related research methodology, including evidence synthesis, handling and analysis of datasets, and statistical techniques. Attendance at formal training courses will be encouraged, and will include the one-week “Randomised Controlled Trials Course” run by CSM and courses in statistics as relevant.

In addition, courses from the Oxford Learning Institute and the Oxford University Computer Sciences on key skills for the completion of a successful DPhil thesis will be available. Additional on the job training opportunities will arise, and the supervisors will encourage the student to pursue such opportunities.

A core curriculum of lectures organized departmentally will be taken in the first term to provide a solid foundation in a broad range of subjects including epidemiology, health economics, and data analysis. Students will attend weekly seminars within the department and those relevant in the wider University. Students will be expected to present data regularly to the department, the research group and to attend external conferences to present their research globally.

Students will have access to various courses run by the Medical Sciences Division Skills Training Team (https://www.medsci.ox.ac.uk/study/skillstraining) and other departments.

Further information:


Please contact: Associate Prof Sally Hopewell ().

How to apply:


The department accepts applications throughout the year but it is recommended that, in the first instance, you contact the relevant supervisors or the Graduate Studies Officer, Sam Burnell (), who will be able to advise you of the essential requirements.

Interested applicants should have or expect to obtain a first or upper second class BSc degree or equivalent, and will also need to provide evidence of English language competence. The application guide and form is found online (https://www.ox.ac.uk/admissions/graduate/applying-to-oxford/application-guide?wssl=1) and the DPhil or MSc by research will commence in October 2019.

For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.


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