Correcting for site-specific biases into population-level effect estimates synthesized from distributed data networks
- Project No: NDORMS-2021/7
- Intake: 2021
Routine healthcare (“real world”) data is a unique source of information to rapidly assess the utilisation and risk-benefit of medicines and devices. Open science initiatives like the European Health Data and Evidence Network (www.ehden.eu) and the Observational Health Data Sciences and Informatics (www.ohdsi.org) provide access to harmonised data from multiple sources for distributed network analyses. Despite the multiple advantages involved, there are also challenges related to local coding practice and heterogeneous clinical practice that can result in differential and site-specific biases. Meta-analysis methods are currently used to pool causal effect estimates, but do not incorporate site-specific biases.
As a working example and clinical use case, we will study the risk-benefit of medicines used for the treatment of COVID19 in different parts of the world, and using different types of real-world data sources (claims, primary care records, inpatient electronic health records). The recent COVID19 outbreak has put healthcare systems under pressure, and resulted in the emergency approval of multiple medicines for compassionate use. As a result, a multiplicity of guidelines and management strategies have been established in different parts of the world, leading to differential use of medicines, database-specific sources of misclassification, and related biases.
This project will therefore:
- Study the risk-benefit of repurposed (anti-viral) as well as adjunctive therapies for the treatment of COVID19 globally
- Investigate methods to effectively integrate evidence from disparate data sources with different biases to draw causal claims from a distributed network analysis
For this purpose, the student will analyse data from multinational healthcare sources previously standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (OMOP-CDM) in collaboration with the European EHDEN (www.ehden.eu) and global OHDSI (www.ohdsi.org) open science communities.
DETAILS OF THE RESEARCH GROUP
The DPhil will be jointly supervised by Prof Prieto-Alhambra, Dr Victoria Strauss, based at the Oxford Centre for Statistics in Medicine (CSM), NDORMS, University of Oxford; A/Prof Patrick Ryan, from Janssen Analytics and the Department of Biomedical Informatics at Columbia University, NYC, USA; and by Prof Marc A Suchard, from the Departments of Biostatistics in the UCLA Fielding School of Public Health at the University of California, Los Angeles, USA.
Prof Daniel Prieto-Alhambra has published extensively in the field of real-world epidemiology, and is recognised internationally as an authority in the use of routine data for pharmaco-epidemiology.
Dr Victoria Strauss is a senior statistician with a long track record in the conducting and analysis of randomized controlled trials and real-world evidence studies.
A/Prof Patrick Ryan is one of the leads of OHDSI and one of the key minds behind the existing analytical tools and pipelines. He is an expert in drug safety research with years of experience in the industry and academic sectors.
Prof Marc A Suchard is a Professor of Biostatistics and one of the main contributors to the OHDSI pipelines for distributed network analyses.
Current DPhil Students within the group: 7
The Centre for Statistics in Medicine (CSM) is located in the Botnar Research Centre. CSM has more than 20 years’ experience in medical statistics, has participated in more than 80 trials, and is the home of the department’s Big Health Data Research, the Oxford Clinical Trials Research Unit, and the EQUATOR Centre. The proposed project would be part of the work of the Big Health Data Research group.
Training will be provided in relevant related research methodology, including the handling and analysis of large datasets, and advanced statistical techniques. Attendance at formal training courses will be encouraged, and will include the "Real world epidemiology Oxford summer school" and advanced statistics courses.
In addition, courses from the Oxford Learning Institute and the Oxford University Computer Sciences on key skills for the completion of a successful DPhil thesis will be available. Additional on the job training opportunities will arise, and the supervisors will encourage the student to pursue such opportunities.
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, health economics, and data analysis.
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 (firstname.lastname@example.org) 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, and will also need to provide evidence of English language competence. The University requires candidates to formally apply online and for their referees to submit online references via the online application system.
The application guide is found online and the DPhil or MSc by research will commence in October 2021.
When completing the online application, please read the University Guide.