Assessing the use of treatments for blood cancers using real world data

PROJECT OVERVIEW

Around a quarter of a million people in the UK are living with blood cancer. These cancers, such as leukaemia, lymphoma, and myeloma, affect a wide age range across the population, with it being the most common type of cancer in children. Patient characteristics, typical treatments received, and health outcomes vary by type of cancer and various other patient-related factors (such as age).  As well as the health burden for the patients affected, blood cancer imposes a substantial economic burden on the health care system with drugs for blood cancers double that of average cancer costs.

Real-world data provides an opportunity to examine the drug prescriptions received and health outcomes realised by patients with blood cancer under routine practice. Different types of real-world data can be best suited for different aspects, with primary care data being valuable for identifying initial presentation, while detailed secondary care records allow for an understanding of treatments pathways. Large scale real-world data also offers the opportunity to better understand the safety profile of treatments, comparative effectiveness of alternative treatment strategies, as well as the financial burden imposed on the health care system.

This DPhil project will involve using real-world data to improve our understanding of blood cancers. Drawing upon multiple national and international datasets, the student will study medication use across a range of blood cancers and assess how their use has changed over time, differs across patient-related factors, and varies across countries. The student will go on to use similar data to undertake comparative studies of different treatment strategies in terms of risk of adverse events and/ or effectiveness.

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 European Health Data & Evidence Network (EHDEN) and Observational Health Data Sciences and Informatics (OHDSI) networks 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 also have the opportunity to work closely with the Oxford Translational Myeloma Centre (https://www.ndorms.ox.ac.uk/research/otmc) .

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).

Key words

• Real-World Evidence
• Cancer
• Epidemiology
• Pharmacoepidemiology
• Pharmacovigilance

KEY PUBLICATIONS

1          Burns R, Leal J, Sullivan R, Luengo-Fernandez R. Economic burden of malignant blood disorders across Europe: a population-based cost analysis. Lancet Haematol 2016; 3: e362–70. https://doi.org/10.1016/s2352-3026(16)30062-x

2          Din NU, Ukoumunne OC, Rubin G, et al. Age and Gender Variations in Cancer Diagnostic Intervals in 15 Cancers: Analysis of Data from the UK Clinical Practice Research Datalink. PLoS One 2015; 10: e0127717. https://doi.org/10.1371/journal.pone.0127717

3          Kim K, Verburgh E, Mitina T, et al. Treatment pathways and clinical outcomes in newly diagnosed multiple myeloma outside Europe and North America: The INTEGRATE study. Int J Hematol 2025; 122: 231. https://doi.org/10.1007/s12185-025-03972-8

4          Huntington SF, de Nigris E, Puckett JT, et al. Real‐world analysis of adverse event rates after initiation of ibrutinib among Medicare beneficiaries with chronic lymphocytic leukemia. Cancer Med 2024; 13: e6953. https://doi.org/10.1002/cam4.6953

5          Goyal RK, Nagar SP, Kabadi SM, Le H, Davis KL, Kaye JA. Overall survival, adverse events, and economic burden in patients with chronic lymphocytic leukemia receiving systemic therapy: Real‐world evidence from the medicare population. Cancer Med 2021; 10: 2690. https://doi.org/10.1002/cam4.3855

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 will commence in October 2026.

Applications should be made to the following programme 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.