Improving the quality of real world evidence by measuring and minimising outcome misclassification using the OMOP common data model and large multinational health data

PROJECT OVERVIEW

This scholarship and work has been proposed to continue and expand work started by the late James (Jamie) Weaver. Jamie was a talented and bright data scientist and DPhil student working with us on the use of methods to minimise the impact of outcome misclassification in real world evidence (RWE). Funding has been secured, from the Medical Sciences Division, Brasenose College, and NDORMS, for this project to continue his important work on this extremely relevant topic; the successful candidate will be assigned to Brasenose College.

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 multiple European and international networks to generate reliable information to inform, amongst others, regulatory decision making and health technology assessments.

Through ongoing collaborations, we leverage multiple international datasets mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model in a federated manner. Previous work led by our student Jamie Weaver uncovered the impact of outcome misclassification on the estimation of background rates of adverse events, and proposed new methods to account for this in future studies [3].

Through this 3-year PhD funded studentship, we aim to investigate how novel methods can be applied to measure and account for outcome misclassification in RWE studies, by researching:

1. The use and application of artificial intelligence (and specifically large language models) for the generation and validation of computable phenotypes
2. The impact of outcome misclassification in different data assets
3. The performance of existing and novel methods to account for outcome misclassification in international RWE studies  

Key References

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

3)     James Weaver, Patrick B. Ryan, Victoria Strauss, Marc A. Suchard, Joel Swerdel, Daniel Prieto-Alhambra. Impact of phenotype error adjustment on background incidence of COVID19 vaccine adverse events of special interest. Link

KEYWORDS

Real world evidence, epidemiology, health data sciences

The Health Data Sciences team

The Health Data Sciences team at the Botnar Institute is a multidisciplinary group including over 40 people including research staff, postdoctoral researchers, and 8 PhD students. 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, epidemiology, and pharmacoepidemiology.

Training

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 Health Data Sciences section at the Botnar Institute (University of Oxford).

The Botnar Institute plays host to the University of Oxford's NDORMS Health Data Sciences and Real World Evidence section, which enables and encourages research and education into the use of large routinely collected health data for the study and improvement of human health. Training will be provided in techniques and methods including epidemiology, pharmacoepidemiology, data sciences, applied artificial intelligence, causal inference, and real world evidence.

A core curriculum of lectures will be taken in the first term to provide a solid multidisciplinary foundation in a broad range of subjects including biology, inflammation, epigenetics, translational immunology, microbiome, and data sciences.  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 Science 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.

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).

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 is found online and the DPhil programme will commence in October 2026.

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.