About the Project
Real-world data (RWD), routinely collected across various healthcare settings, are increasingly used in observational studies, which are now recognised as valuable complements to traditional randomised controlled trials for informing patient care, healthcare delivery, and policy-making.
The real-world evidence (RWE) derived from RWD is strengthened when institutions collaborate through network studies that rely on Common Data Models (CDMs) for both centralised and federated approaches. CDMs are defined frameworks designed to standardise the complex relational topologies and structure of healthcare data across diverse data sources and harmonise their representation, ensuring that analyses are consistent, comparable, and generalisable.
Our team has extensive expertise in the Observational Medical Outcomes Partnership (OMOP) CDM, one of the most widely used CDMs, and in the extract, transform, and load (ETL) process that translates source data into this model. We are active data partners in international federated networks and have access to multiple clinical RWD sources. Moreover, we have experience in graph theory, graph machine learning, descriptive complexity, verification, automata theory, and computational biomedicine. A growing body of research and tools exists to validate and assess the quality of RWD once transformed to the OMOP CDM (1–4). However, no computational models and methods have been proposed to measure the structural similarity and distance between relational source RWD and their transformed versions. This project aims to address this critical gap, strengthen the transformation process and enhance the validity of the resulting data through:
- Developing novel measures of structural similarity and distance between RWD and their transformed OMOP CDM versions by leveraging principles from algorithmic graph theory and approaches to structural data comparison.
- Creating tools to detect missing, incorrect, incomplete or redundant data transformations based on the novel measures developed in point 1
- Creating expressive algorithmic models that, using the results of the tools developed at point 2, will help improve and automate structural mapping corrections within the ETL process
This is a 3-year DPhil project between the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences and the Computer Science department of the University of Oxford.
Keywords
Real-world data, health data sciences, data standardisation, data harmonisation, OMOP CDM, graph theory
The team
The Health Data Sciences (HDS) team at the Botnar Institute is a multidisciplinary group including over 40 people: 10 research staff, 16 postdoctoral researchers, and 8 PhD students and several visiting scientists. Our team includes colleagues from multiple and diverse backgrounds and geographies, and from complementary areas of knowledge, necessary for the completion of research studies, from design to reporting. We have extensive expertise in health data sciences, computer science, data standardisation and harmonisation, machine learning, epidemiology, pharmacoepidemiology, and pharmacogenomics.
The collaboration with the Department of Computer Science will provide an even more interdisciplinary environment, including also mathematics, computer science, and bioinformatics to enable the development of novel computational approaches.
Supervision
- Main supervisor: Associate Professor Antonella Delmestri https://www.ndorms.ox.ac.uk/team/antonella-delmestri
- Co-supervisor: Associate Professor Sandra Kiefer https://www.cs.ox.ac.uk/people/sandra.kiefer/
- Co-supervisor: Dr Marta Pineda-Moncusi https://www.ndorms.ox.ac.uk/team/marta-pineda
Training
Alongside departmental training opportunities listed below, the HDS group will provide hands-on training in RWD using medical records and genetic data. The student will work on their unique project in an experienced and collaborative supervisory team. To enrich their studies, the student will be encouraged to attend relevant conferences, with financial travel support.
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 a variety of techniques and methods, including health data sciences, applied artificial intelligence, and real-world evidence.
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, fortnightly Health Data Sciences meetings, 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 our wide range of collaborators in the Observational Health Data Sciences and Informatics (OHDSI), European Health Data and Evidence Network (EHDEN), and related open data science communities, to ensure additional guidance, training and support.
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).
As an interdepartmental DPhil student, the candidate will also have free access to the comprehensive MPLS Researcher Training & Development programme with many courses tailored to doctoral researchers, including training in scientific writing and publication, presentation and communication skills, time and project management, supervisor relationships and viva preparation, alongside dedicated initiatives such as the MPLS DPhil Bootcamp and workshops on successfully completing a DPhil.
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 are found online, and the DPhil or MSc by research will commence in October 2027.
Applications should be made to one of 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.
Applications open mid-September
Application deadline: 12:00 on 1st December
References
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54–60. doi: 10.1136/amiajnl-2011-000376
Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, Estiri H, Goerg C, Holve E, Johnson SG, Liaw ST, Hamilton-Lopez M, Meeker D, Ong TC, Ryan P, Shang N, Weiskopf NG, Weng C, Zozus MN, Schilling L. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016;4(1):1244. doi: 10.13063/2327-9214
Blacketer C, Defalco FJ, Ryan PB, Rijnbeek PR. Increasing trust in real-world evidence through evaluation of observational data quality. J Am Med Inform Assoc. 2021;28(10):2251–7. doi: 10.1093/jamia/ocab132
Blacketer C, Voss EA, DeFalco F, Hughes N, Schuemie MJ, Moinat M, et al. Using the Data Quality Dashboard to improve the EHDEN network. Appl Sci (Basel). 2021;11(24):11920. doi: 10.3390/app112411920
Kiefer S, Schweitzer P, Selman E. Graphs Identified by Logics with Counting. Association for Computing Machinery. 2022;23(1):1-31 doi: 10.1145/3417515
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