About
Marta García-Fiñana (PhD, FRSS) is Professor of Biostatistics and Head of the Department of Health Data Science at the University of Liverpool. She joined the University 2003, after completing her BSc in Theoretical Physics and MSc in Statistics at the University of Cantabria (Santander, Spain).
Her main research focuses on the development and application of multivariate statistical techniques. This includes the generation of models that predict the clinical status of a patient using their clinical records with time-dependent (and sometimes spatial) information. Examples of clinical applications are in the areas of diabetes, epilepsy, liver and bladder cancer, and stroke among others. Development and application go hand in hand in Marta’s research portfolio as reflected by the variety of research outputs, sources of funding, research activities and collaborative links.
Marta has also developed a track record in the field of Stereology. Stereological methods are used to quantify geometrical parameters of biological structures (e.g., volume, surface area, density) following a proper sampling design. One of Marta’s key contributions is the generalisation of the variance in Cavalieri sampling, allowing discontinuity points of the area function being represented with fractional derivatives.
During 2020 and 2021, Marta focussed on Covid-19 research. She was a member of the Liverpool SMART Covid-19 team that assessed the accuracy of the Innova Lateral Flow antigen Covid-19 testing in asymptomatic individuals. This was the first quality assurance study conducted on mass rapid testing in the asymptomatic population. The results were put together in a report for the Scientific Advisory Group for Emergencies (SAGE) in November 2020 and were published later in the British Medical Journal. The research gained worldwide media attention during 2021 and 2022 (including an interview for BBC Radio 4 and references to this work on the BBC News website, among others). Additional insights on the performance of lateral flow devices in community testing were published in the journals Lancet and Science. Other contributions include the evaluation of social and spatial inequalities for COVID-19 management, and of testing strategies in care homes. Studies provided relevant insights for public health teams and policymakers when considering community approaches to Covid-19 testing.
I am interested in applications from potential PhD students who are keen to pursue a PhD in the areas of prediction modelling, early diagnosis and classification using large healthcare datasets.
Prizes or Honours
- Florence Nightingale award for Excellence in Healthcare Data Analytics (Royal Statistical Society, 2023)
- Research Impact of the Year 2022 (University of Liverpool, 2022)
- Outstanding Contribution During the COVID-19 Pandemic 2022 (University of Liverpool, 2022)