About the Project
Given the limited availability of clinical trial data and the high prevalence of off-label prescribing in paediatric oncology, real-world data is critical for evaluating the uptake and safety of medicines used in the area. For childhood cancers, treatments are constantly changing and there is a need to learn from real-world use. However, because childhood cancers are rare, individual hospitals often struggle to find enough patients receiving a specific medicine to study it in detail. Using data…
from a single hospital also misses the opportunity to learn from the different approaches used elsewhere. Although multi-centre studies allow for larger study populations and an opportunity to learn from the heterogeneity in practice across hospitals, these have historically been time-consuming and costly to conduct.
This DPhil project will leverage recent advances in the standardisation of datasets to the OMOP Common Data Model (CDM) to conduct multi-centre studies focused on rare pediatric cancers, such leukaemias (such as acute lymphoblastic leukaemia and acute myeloid leukaemia), lymphomas (such as Burkitt lymphoma), and solid tumours (such as brain and kidney tumours). Example datasets that may be used include data from Great Ormond Street Hospital, London, England, and The Hospital for Sick Children, Toronto, Canada, both of which have already been mapped to the OMOP CDM. Initial research studies would characterise the uptake and utilisation of novel medicines, with the student then going on to use similar data to undertake comparative studies of different treatment strategies in terms of risk of adverse events.
Supervisors
Key words
Real-World Evidence, Epidemiology, Pharmacoepidemiology, Pharmacovigilance, Paediatrics
Training Opportunities
Alongside departmental training opportunities listed below we will ensure hands-on training in real world data analysis using medical records and genetic data from the Pharmaco- and Device epidemiology research group. This interdisciplinary research group contains a variety of students and post-doctoral researchers with expertise in health data science, epidemiology, pharmacogenomics, and machine learning. The student will work on their unique project within an experienced and collaborative supervisory team. The student will also be embedding within our international Observational Health Data Sciences and Informatics (OHDSI) network to ensure additional analytical guidance, training and support. A student would be supported to attend relevant conferences to enrich their studies and financial support will be made available for travel to conferences.
The Botnar Research Centre plays host to the University of Oxford's Institute of Musculoskeletal Sciences, which enables and encourages research and education into the causes of musculoskeletal disease and their treatment. Training will be provided in techniques including data analysis, research design, protocol writing, and publishing.
A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including musculoskeletal biology, inflammation, epigenetics, translational immunology, data analysis and the microbiome. Students will also be required to attend regular seminars within the Department and those relevant in the wider University.
Students will be expected to present data regularly in Departmental seminars, the Pharmco- and device epidemiology group and to attend external conferences to present their research globally, with limited financial support from the Department.
Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other Departments. All students are required to attend a 2-day Statistical and Experimental Design course at NDORMS (information will be provided once accepted to the programme).
How to Apply
Please contact the relevant supervisor(s), to register your interest in the project, and, if required, the departmental Education Team (graduate.studies@ndorms.ox.ac.uk), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.
Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil or MSc by research will commence in October 2027.
Applications should be made to one of the following programmes using the specified course code: D.Phil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1)
For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.
Applications open mid-September
Application deadline: 12:00 on 1st December
References
- Declerck J, Lee J, Sen A, et al. The Potential to Leverage Real-World Data for Pediatric Clinical Trials: A Proof-of-Concept Study. J Med Internet Res. 2025;27:e72573. Published 2025 May 30. doi:10.2196/72573
- McMahon AW, Quinto K, Abernethy A, Corrigan-Curay J. Summary of Literature on Pediatric Real-world Evidence and Effectiveness. JAMA Pediatr. 2021;175(10):1077–1079. doi:10.1001/jamapediatrics.2021.2149
- Eric J. Slora, Donna L. Harris, Alison B. Bocian, Richard C. Wasserman. Pediatric Clinical Research Networks: Current Status, Common Challenges, and Potential Solutions. Pediatrics (2010) 126 (4): 740–745. https://doi.org/10.1542/peds.2009-3586
- Blacketer C, Schuemie MJ, Moinat M, et al. Advancing Real-World Evidence Through a Federated Health Data Network (EHDEN): Descriptive Study. J Med Internet Res. 2025;27:e74119. Published 2025 Aug 7. doi:10.2196/74119
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