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Methods for observational risk-benefit studies of medical devices: An analysis of big data and simulation studies

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  • Full or part time
    Prof D Prieto-Alhambra
    Dr S Ali
    Prof G Collins
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Reference number: NDORMS 2018/2

Medical devices are used in many areas of surgery. Recently published EU regulation will likely imply the need for numerous post-marketing device surveillance studies. There is however a scarcity of methodological literature on the performance of existing statistical models to minimise confounding in observational studies comparing the risk and benefit of different medical devices. Many methods exist and are widely applied in observational drug safety and comparative effectiveness research. However, there are challenges (e.g., surgeon characteristics, learning curve with new device/s) and opportunities (e.g., less clear clinical guidance) specific to medical device epidemiology, which warrant new research.

We aim to assess the performance of different statistical methods for the observational study of the risk/s and benefit/s of orthopaedic devices as used in actual practice conditions and in potentially all NHS patients.

To do so, we will use routinely collected big health data (i.e. information from national registries, audits, and pseudonymised NHS records) as well as simulated datasets.

These will be analysed using different approaches including propensity scores, disease risk scores, multi-level modelling, instrumental variables, and case-only designs. These different observational analyses will be applied to clinical use cases and compared to on going surgical randomized controlled trials. In addition, they will also be used to analyse simulated datasets for the study of their ability to minimize confounding and related bias in comparative device risk-benefit studies.

Themes: Big Health Data research


The DPhil will be jointly supervised by Associate Prof Prieto-Alhambra, Dr M Sanni Ali, and Prof Gary Collins, all based at the Centre for Statistics in Medicine (CSM), NDORMS, University of Oxford.

Associate Prof Daniel Prieto-Alhambra has published extensively in the field of pharmaco-epidemiology, and is recognised internationally as an authority on use of routine data for musculoskeletal pharmaco- and device epidemiology.

Dr M Sanni Ali is a Senior Research Associate in Pharmaco-epidemiology. He has extensive expertise in the use, validation and development of pharmaco-epidemiological methods, both for the analysis of routinely collected data as well as in simulated datasets.

Prof Gary Collins’ research interests are focused on methodological aspects surrounding the development and validation of multivariable prediction models and he has published extensively in this area. He has a particular research focus on the role that big data has in evaluating prediction models.

Current DPhil Students within the group: 4


The Centre for Statistics in Medicine (CSM) is located in the Botnar Research Centre. CSM has more than 20 years experience in medical statistics, has participated in more than 80 trials, and is the home of the department’s Big Health Data Research, the Oxford Clinical Trials Research Unit, and the EQUATOR Network. The proposed project would be part of the work of the Big Health Data Research group.

Training will be provided in relevant related research methodology, including the handling and analysis of large datasets, and advanced statistical techniques. Attendance at formal training courses will be encouraged, and will include the "Real world epidemiology Oxford summer school" and advanced statistics courses.

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.


Associate Prof D Prieto-Alhambra: [Email Address Removed]


The department accepts applications throughout the year but it is recommended that, in the first instance, you contact the relevant supervisor(s) or the Graduate Studies Officer ([Email Address Removed]) 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 University requires candidates to formally apply online and for their referees to submit online references via the online application system.

The application guide and form is found online and the DPhil or MSc by research will commence in October 2018.

When completing the online application, please read the University Guide:

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