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  • Project No: NDORMS 2024/09
  • Intake: 2024


Precision medicine in oncology is changing the prognosis of numerous cancers. However, the identification of very specific and rare cancer subtypes is leading to challenges in the recruitment of enough subjects for randomised controlled trials.

Real world evidence (RWE) is generated by leveraging and processing large routinely collected health data. Despite difficulties in the analysis of such information for causal inference purposes, RWE has recently been shown as a reliable source of data when used using adequate methods for trial emulation [1, 2].

We participate in various European and international networks partnering with an interest in the generation of reliable RWE to improve the diagnosis and management of different cancers [3]. These provide a unique opportunity for the analysis of multiple datasets mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model in a federated analytics infrastructure.

As research in oncology focusses on more rare or specific types of breast, lung and prostate cancer, a combination of trials and observational (real world) data are needed to inform the feasibility of conducting research into different subcategories of cancer.

Through this 3-year studentship, we aim to investigate how RWE can be used to improve patient health and prognosis in oncology, by researching:

  1. How can we identify and phenotype different types of cancer using real world data from Europe and the United States?
  2. Can RWE be used to feed synthetic control arm trials in oncology?
  3. Can target trial emulation methods using RWE help us understand the safety and benefits of medicines, devices, vaccines, and surgery in oncology? 


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. Our interdisciplinary research groups contain a variety of students and post-doctoral researchers with expertise in health data science, epidemiology, and clinical trial methods. The student will work on their unique project within an experienced and collaborative supervisory team.

A core curriculum of lectures will be taken to provide a solid foundation in a broad range of subjects including statistics, epidemiology, and big data analysis. All students will attend a 2-day Statistical and Experimental Design course at NDORMS, and our residential 5-day Real World Evidence Summer School. Students will also attend regular seminars within the department and have access to a variety of other courses run by the Medical Sciences Division Skills Training Team

Finally, the student(s) will regularly present data in departmental seminars, Pharmaco- and device epidemiology group lab meetings, and the International Society of Pharmacoepidemiology meetings, among others. 


1)    Wang SV, Schneeweiss S, et al. Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials. JAMA. 2023;329(16):1376-1385. doi:10.1001/jama.2023.4221

2)    Hernandez-Diaz S, et al. Emulating a Target Trial of Interventions Initiated During Pregnancy with Healthcare Databases: The Example of COVID-19 Vaccination. Epidemiology. 2023 Mar 1;34(2):238-246. doi: 10.1097/EDE.0000000000001562



Real world evidence, oncology, target trial emulation, epidemiology, data sciences


It is recommended that, in the first instance, you contact the relevant supervisor(s) and the Graduate Studies Office ( 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 in a relevant subject and will 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 2024.

Applications should be made to one of the following programmes using the specified course code:

-       DPhil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1) 

For further information, please visit