Reference Number: NDORMS 2018/9
Big clinical data represent the future of clinical research while offering unique opportunities and invaluable developments. However, big health data management presents a huge challenge for researchers and scientists who need to rely on high-level Information Technology (IT) experts.
Although this type of experts are able to provide the missing link in the research chain, their provision is scarce, their expertise expensive and their intervention very time consuming.
To overcome these limitations, to facilitate and improve the development of research projects, new database management software tools are needed. Their implementation would improve dramatically clinical research based on big data allowing the identification of optimal database models, query methods and establishing a national coding system. This automation not only would ensure research reproducibility and consistency, but also would empower a wider audience to perform reliable and solid data management on big clinical data. This would save many resources in terms of both time and money.
We will develop specialized software for the database management of observational research studies allowing for the different research designs, including case-control, cohort and cross-sectional studies to investigate causal factors. We will identify an optimal DataBase Management Systems (DBMS) and web software framework for this purpose. We will embed epidemiology and clinical expertise in the software to create a user-friendly web interface for big clinical data management. We will establish a national medical and product coding system working with other experts in the field by creating a coding national network.
The derived tools will be tested in a use case including routinely collected data from UK Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics (HES: Inpatient, Outpatient and PROMS), Office for National Statistics (ONS) and Index of Multiple Deprivation (IMD).
Big Health Data research
DETAILS OF THE RESEARCH GROUP
The DPhil will be jointly supervised by Dr Antonella Delmestri are Associate Prof Prieto-Alhambra, who are both part of the NDORMS ‘Big Health Data’ research group.
Dr Antonella Delmestri is a Senior Database Manager with vast and long term expertise in big clinical data (e.g. CPRD, HES, ONS, IMD, etc.), computer science and software engineering. She has an outstanding publication history in epidemiology, controlled clinical trials and computer science.
Associate Prof Prieto-Alhambra has published extensively in the field of pharmaco-epidemiology, and is recognized internationally as an authority on use of routine data for musculoskeletal pharmaco- and device epidemiology.
The Botnar Research Centre plays host to the University of Oxford's Institute of Musculoskeletal Sciences and Centre for Statistics in Medicine.
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 the "Advanced musculoskeletal epidemiology UK-RIME summer school".
In addition, courses from the Oxford Learning Institute and the Oxford University Computer Sciences on key skills for the completion of a successful PhD 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.
Dr Antonella Delmestri: [email protected]
HOW TO APPLY
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 protected]
) 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: https://www.ox.ac.uk/admissions/graduate/applying-to-oxford/application-guide?wssl=1