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Jacqueline Birks


NIHR - OXBRC Senior Medical Statistician

I have an extensive range of experience, covering quantitative genetics, clinical trials, individual patient data meta-analysis, analysis of longitudinal data, systematic reviews, and diagnostic and prognostic tests. My interests range over a number of clinical specialities, including surgery, cardiology, emergency medicine, dementia, and geriatrics. I am an advisor for the South Central Research Design Service (RDS), and team lead for the RDS team at the Centre for Statistics in Medicine (CSM).

I am involved in the design and analysis of Oxford Biomedical Research Centre (OXBRC) clinical trials comparing the introduction of electronic devices with paper charts for patient data collection. OxBRC has completed one trial comparing an app with paper charts for pregnant women to use at home to monitor their blood glucose and another comparing a tablet computer with paper charts for recording inpatients' vital signs. Not only do these trials test interventions that may improve patient outcomes, they also provide large electronic patient datasets that can be used for further research.

One challenging problem that large electronic patient datasets will help us solve is that of early warning scores. These scores are derived from a patient’s current vital signs and are routinely used in hospitals to identify patients who are deteriorating. However, there is little evidence that they work. I am interested in using statistical methods to develop a risk prediction model for deterioration that takes into account both current and previous vital signs and patient characteristics. 

I am evaluating a risk prediction algorithm for unidentified colorectal cancer, derived using machine learning methods and routine patient data on complete blood count from the Clinical Practice Research Datalink (CPRD) database. I am also using this CPRD dataset to develop a risk prediction model using a statistical model. As risk prediction models need to be tested in practice, I am working on trial designs for this purpose.

Clinical trials that compare a new non-surgical intervention with an existing surgical intervention often provoke strong criticisms. I have been the statistician on such a trial, the International Subarachnoid Aneurysm Trial (ISAT), for many years. This trial compared neurosurgical clipping with endovascular coiling to treat ruptured intracranial aneurysms. The first publication in 2002 showed a clear benefit of coiling compared with surgery, but some have questioned the longer-term benefits. It is therefore important to follow up patients, as we have been doing for nearly 20 years in ISAT. Mortality can be assessed using routinely collected data, but the problem I am interested in is dealing with missing data for the patient-reported outcomes.

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