Surgical interventions can have profound effects on people’s lives, both in terms of benefits and (potentially) in terms of risks or complications. It is therefore essential that surgical interventions should be evaluated appropriately and accurately to inform clinical decisions.
Although randomized controlled trials (RCT) provide gold standard evidence, surgical RCTs are expensive, challenging (given pragmatic, ethical, and other considerations), and hence scarce in the scientific literature. In addition, RCTs tend to use restrictive eligibility criteria therefore limiting the external validity (generalizability) of their findings to relatively healthy populations.
Conversely, routinely collected data (i.e. anonymized data from national registries, audits, and computerized NHS records) provide a unique opportunity to evaluate surgical interventions in potentially any NHS user, widening our ability to gather information on the performance of surgical and medical alternatives in different patient groups. However, given the observational nature of such studies, confounding (by indication) is a major challenge that needs to be accounted for to avoid biased results. Methods exist to minimize such confounding and other biases, which are widely used in pharmaco-epidemiology (drug safety studies), but these have been seldom tested for the study of surgical treatments.
The project will focus on two clinical scenarios for the risk-benefit evaluation of 1.two different surgical alternatives (unicompartmental vs total knee replacement) for the treatment of knee arthritis, and 2.a surgical procedure (bariatric surgery) compared to no surgery for the management of severe obesity. A number of different methodological approaches will be examined and compared (including feature selection methods to identify predictive factors) and propensity score, disease risk scores, instrumental variables, and case-only designs to study the risks and benefits of these treatments. These findings will be supplemented with simulation studies to investigate the performance and robustness of the different design parameters and statistical methods proposed in different scenarios, and to offer solutions to the challenges encountered while analyzing big data.
The project will use routinely collected data from the UK Clinical Practice Research Datalink, Hospital Episode Statistics (HES), and National Joint Registry, as well as bespoke (created ad-hoc) simulated datasets.
The DPhil will be jointly supervised by Associate Prof Prieto-Alhambra, Dr M Sanni Ali, and Dr Sara Khalid, (all members of the NDORMS ‘Musculoskeletal Pharmaco- and Device epidemiology’ research group), and Prof Gary Collins, Professor of Medical Statistics and Deputy Director of the Oxford Centre for Statistics in Medicine (CSM).
Associate Prof Daniel Prieto-Alhambra has published extensively in the field of pharmaco-epidemiology, and is recognised internationally as an authority on use of routine data for musculoskeletal pharmaco- and device epidemiology.
Dr M Sanni Ali is a Senior Research Associate in Pharmaco-epidemiology. He has extensive expertise in the use, validation and development of pharmaco-epidemiological methods, both for the analysis of routinely collected data as well as in simulated datasets.
Dr Sara Khalid is lead analyst for the Health Services Delivery and Musculoskeletal Pharmaco-epidemiology group. She has an Oxford DPhil in Engineering Science, and has an excellent track record and experience in the use of big data methods including machine learning and similar methods.
Prof Gary Collins‘ research interests are focused on methodological aspects surrounding the development and validation of multivariable prediction (prognostic) models and has published widely in this area. He has a particular focus on the role that big data has in evaluating prediction models.
Current DPhil Students within the pharmaco-epidemiology group: 3
The Botnar Research Centre plays host to the University of Oxford’s Institute of Musculoskeletal Sciences and Centre for Statistics in Medicine.
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 the “Advanced musculoskeletal epidemiology UK-RIME summer school”.
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 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.
Associate Prof D Prieto-Alhambra: Daniel.firstname.lastname@example.org