Evaluating methods for signal detection of drug side effects in real world data (NDORMS-2025/1)

Adverse drug reactions (ADRs) are unintended, harmful events attributed to the use of medicines. In modern healthcare, ADRs pose a significant challenge due to the complexity of therapeutics, an aging population, and rising multimorbidity.

By capturing drug prescriptions and health outcomes under routine practice, routinely collected health care data offers a valuable opportunity to generate evidence and identify ADRs. Using such data for signal detection can, however, be challenging due to potential for both false positives and negatives caused by underlying trends in the data, patient-level confounding factors, and the potential for reverse causality. There are several existing methods that can be used to detect ADRs, however further research on their strengths and limitations when applied to real-world data is required. Moreover, given the particular characteristics of real-world data, extensions to existing methods and the development of new methods may be warranted.

This DPhil project will involve the evaluation, development, and application of epidemiological methods using real world data to detect ADRs. Using real-world data, the student may compare methodologies such as sequence symmetry analysis, tree-based scan statistic, and other self-controlled methods. Method development opportunities will also be available for the student to extend existing approaches. The student would then apply the learnings from this initial methodological work on selected clinical examples using real-world data.

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.

NDORMS hosts the Centre for Statistics in Medicine, a centre committed to improving the standard of medical research methodology through research and training on research and methods development. This enables and encourages research and education to champion transparent and complete reporting of health research through reporting guidelines and training provision. A core curriculum of lectures will be taken to provide a solid foundation in a broad range of subjects including statistics, epidemiology, machine learning and big data analysis. All students will be required to attend a 2-day Statistical and Experimental Design course at NDORMS and the Real World Epidemiology: Oxford Summer School. Students will be required to attend regular seminars within the Department and have access to a variety of other courses run by the Medical Sciences Division Skills Training Team and the wider University. Finally, the student(s) will be expected to regularly present data in Departmental seminars, the Pharmco- and device epidemiology group and within our external EHDEN and OHDSI collaborators.

SUPERVISORS:

• Dr Edward Burn https://www.ndorms.ox.ac.uk/team/edward-burn
• Dr Danielle Newby https://www.ndorms.ox.ac.uk/team/danielle-newby
• Dr Annika Jödicke https://www.ndorms.ox.ac.uk/team/annika-jodicke
• Dr Daniel Prieto-Alhambra https://www.ndorms.ox.ac.uk/team/daniel-prieto-alhambra

Key words: Real World Evidence, Drug Safety, Adverse Drug Reactions, Pharmacoepidemiology, Pharmacovigilance

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 Office (graduate.studies@ndorms.ox.ac.uk, 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 also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the D.Phil will commence in October 2025.

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.

The Botnar Institute is a proud supporter of the Academic Futures scholarship programme, designed to address under-representation and help improve equality, diversity and inclusion in our graduate student body. The Botnar and the wider University rely on bringing the very best minds from across the world together, whatever their race, gender, religion or background to create new ideas, insights and innovations to change the world for the better. Up to 50 full awards are available across the three programme streams, and you can find further information on each stream on their individual tabs (Academic futures | Graduate access | University of Oxford).