As COVID-19 vaccines are being rolled out globally, there is an increasing use of different study designs to evaluate real-world performance of vaccines, such as case-control study design, cohort study design, test-negative design, and screening methods. Although several guidelines on their implementation have been published, there is a scarcity of methodological studies comparing the performance of different study designs for the comparative effectiveness of vaccines [1, 2]. Vaccine effectiveness (VE) is a composite of direct and indirect effects conferred by the vaccine, i.e. immunological response and herd immunity, respectively. However, there is little guidance on what part of VE can be calculated in different study designs. In addition, time-varying aspects are important to be taken into account in VE studies. For instance, approaches to deal with time-varying exposure vary between different study designs[3]. It is more challenging when multiple doses of a vaccine need to be accounted for, which is the case for most of COVID-19 vaccines. The stability of vaccine effectiveness change over time due to the status of pandemic, emergence of variants as well as vaccination uptake. It is unclear how different time-varying aspects should be best dealt with and what impact different approaches have on underestimating or overestimating VE. Lastly, real world data is subject to confounding where the source of confounding could come from either the individual (e.g. medical history) or population level (e.g. local infection rates). It remains unclear how multilevel confounding should be dealt with in vaccine studies.
Distributed database network research has been used to rapidly produce real-world evidence on drug effectiveness and has strategies to deal with treatment effect heterogeneity. However, it remains unclear how network research on VE should be conducted best, especially when heterogeneity not only comes from data sources and health systems, but also government policies to control the pandemic, vaccination coverage and the environment (e.g. status of outbreak).
This research will aim to:
1. review the use of methods for VE research;
2. assess the appropriateness/ strengths and limitations of alternative study designs;
3. perform a (network) VE study to establish the stability of VE over time
To achieve these, systematic review and simulation studies will be used to summarise previous and new knowledge on different modelling aspects of VE research. Multinational healthcare data that has been previously standardized to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) will be also used to test these approaches. The appointed researcher will collaborate with the European Health Data & Evidence Network (EHDEN) and global Observational Health Data Science and Informatics (OHDSI) open science communities.
DETAILS OF THE RESEARCH GROUP
The PhD project will be jointly supervised by Dr Victoria Strauss, Dr Edward Burn, Dr Annika Jödicke, and Prof Prieto-Alhambra, all based at the Centre for Statistics in Medicine and the Pharmaco- and Device epidemiology group at the Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences.
Dr Victoria Strauss is a lead statistician specialising in designing and analysing studies using routinely collected data, including CPRD. She has been co-leading multinational studies of COVID-19 vaccine safety with Prof Prieto-Alhambra and is the methodological lead for the study vaccine effectiveness against long COVID-19 using CPRD.
Dr Edward Burn is a postdoctoral research associate in real world health economics. He has extensive experiences in network studies and leads the analysis of COVID-19 vaccine network research.
Dr Annika Jödicke is a postdoctoral research assistant in pharmacoepidemiology with a background in pharmaceutical sciences. She has experience working with real-world data, including CPRD and is the co-principal investigator of a recent project evaluating vaccine effectiveness against long COVID.
Prof Prieto-Alhambra has an extensive publication record using routinely collected data (e.g., CPRD), with several recent publications on COVID-19 phenotyping, risk prediction, and treatment safety. He leads several COVID-19 vaccine grants and leads one methodological work on vaccine safety methods using network data.
Current DPhil Students within the research group: 10
Current Postdocs within the research group: 7
TRAINING
The Botnar Research Centre plays host to the University of Oxford's Institute of Musculoskeletal Sciences and Centre for Statistics in Medicine (CSM).
Training will be provided in relevant related research methodology, including the handling and analysis of large health datasets, and advanced statistical and machine learning techniques, as well as in patient and public engagement for research. Attendance at formal training courses will be encouraged, and will include the "Real world epidemiology” Oxford summer school and the "Big Data and Machine Learning for Healthcare" modules.
In addition, courses from the Oxford Centre for Teaching and Learning, the Medical Sciences Division Skills Training and the Oxford University Computer Sciences Department on key skills for the completion of a successful PhD thesis will be available. Additional on-the-field training opportunities will arise, and the supervisors will encourage the student to pursue such opportunities. Further, the OHDSI global community of 300+ researchers will provide training and opportunities for international collaboration stretching beyond the project.
A core curriculum of lectures organized departmentally will be taken in the first term to provide a solid foundation in a broad range of subjects including epidemiology, machine learning, and statistics. Students will attend weekly seminars within the department and those relevant in the wider University.
Students will be expected to present data regularly to the department, the research group and to attend external conferences to present their research globally.
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 Officer, Sam Burnell ([Email Address Removed]), 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 DPhil will commence in October 2022.
Applications should be made to the following program using the specified course code:
DPhil in Musculoskeletal Sciences (course code: RD_ML2)
For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.
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