Unlocking Insights from Real-World Evidence: Exploring the Safety of Drug Combinations

PROJECT OVERVIEW

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. While ADRs can occur with a single medication, the risk of experiencing ADRs increases when multiple medicines are taken simultaneously. Patients who experience ADRs face significantly higher odds of all-cause mortality, emphasizing the importance of assessing and generating evidence to improve patient outcomes.

Real-world data offers a valuable opportunity to generate evidence and identify ADRs while evaluating the safety of combinations of medicines. By integrating results from various approaches, such as medical records and genetic studies, we can triangulate evidence and obtain more reliable answers, leveraging the strengths of each approach and mitigating potential biases.

This DPhil project will involve the innovative application and development of epidemiological methods using medical records and genetic data to assess the safety of drug combinations. Using real-world healthcare data, the student may employ methodologies such as target trial emulation, survival analysis, self-controlled case-series, prescription symmetry analysis, while using methods such as Mendelian randomisation with genetic data. Method development opportunities will also be available for the student to extend existing approaches. There will be flexibility for a student to focus on a specific disease area or take a more general approach to assessing the impact of drug combinations on risks of ADRs.

This DPhil project offers a unique platform for the exploration of novel methodologies, the integration of diverse data sources, and the pursuit of innovative solutions to address the challenges associated with drug combinations, ADRs and their impact on patient outcomes.

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 https://www.medsci.ox.ac.uk/study/skillstraining 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.

KEY PUBLICATIONS

1. Lawlor, D. A., Tilling, K. & Smith, G. D. Triangulation in aetiological epidemiology. International Journal of Epidemiology 45, 1866–1886 (2016). https://doi.org/10.1093/ije/dyw314
2. Hennessy S et al. Pharmacoepidemiologic methods for studying the health effects of drug-‎drug interactions. Clin Pharmacol Ther 2016;99(1):92-100.‎ https://doi.org/10.1002/cpt.277
3. Bykov K, Franklin JM, Li H, Gagne JJ. Comparison of self-controlled designs for evaluating ‎outcomes of drug-drug interactions: Simulation study. Epidemiology 2019;30(6):861-866. https://doi.org/10.1097/EDE.0000000000001087   ‎

How to Apply 

It is recommended that, in the first instance, you contact the relevant supervisor(s) and 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 2024.

Applications should be made to 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.