Deputy Head of Department

Deputy Lead of NIHR Maudsley Biomedical Research Centre theme "Trials, Prediction and Genomics"

I am a Professor of Medical Statistics and Statistical Learning and lead of the NIHR Maudsley BRC "Prediction Modelling" group. My interest is applying statistical and machine learning methods to develop and implement robust risk prediction and treatment outcome models. I am also interested to identify predictors, mediators, and moderators of treatment success and applying model-based cluster analysis methods to identify subgroups among psychiatric patients.

I was involved in the development of a transdiagnostic risk predictor for psychoses, a model to predict the recurrence of depression, an inexpensive biomarker to identify young people at increased risk of self-harm or suicide, and transdiagnostic psychopathology and PTSD risk calculators for trauma-exposed young people using the E-Risk Longitudinal Twin Study. 

As a Lead Trial Statistician, I am responsible for overseeing the statistical aspects of several clinical trials within the IoPPN. I am further interested in model selection problems and - a blast from the past - in the evolution of the social system in primates.

Education

As Education Lead I lead the educational activities of our department which involves bringing statistics to life for a variety of students and researchers. 

I am teaching introductory and advanced statistics courses for MSc, DClinc Psych and PhD students and researchers of the Institute of Psychology, Psychiatry and Neuroscience including Introduction to statistics, Mediation and moderation, Model selection, Multiple testing, Structural equation modelling, Scale development and Statistical learning methods for prognostic models and stratified medicine. 

Innovation Scholars Programme "Big data skills training for the health workforce"

I lead the Health Data Science online training centre, which is a part of the Innovation Scholar Program funded by UK Research and Innovation (UKRI). The modules offered within this program aim to provide an introduction and comprehensive training in utilizing diverse large-scale data analysis techniques. These modules enable participants to effectively handle the expanding repository of electronic health record data for both research purposes and practical applications in real-world settings.

I am the programme lead of the MSc in “Applied Statistical Modelling and Health Informatics, which is centred on the disciplinary strength and academic excellence of our department. The MSc/PGCert/PGDip will consist of 6-week modules (2 weeks of lectures, seminars and computer practicals combined with TEL support of home study). The unique structure of the programme is ideal for (future) researchers in industry and academia to obtain methodologi­cal skills that are in demand and boost their research excellence, employability and career as well as for graduates with a degree in psychology, biology, mathematics, statistics, computer science or economics who want to start a career in the exciting world of modern health research.

Maudsley BRC Prediction Modelling Group

In recent years there has been a shift towards stratified (personalized) medicine in which individ­ual characteristics or biomarkers are used to identify patients who are more likely to respond to treatment. In the era of “Big Data”, prediction modelling cannot rely on classical statistical methods and computer-intensive machine learning methods are increasingly needed. However, apply­ing such methods in mental health research involve many methodological challenges such as missing data, unbalanced groups, popula­tion substructure, multi-centre trials, multicollinearity, measurement error, and different measures for the same cognitive construct. We, therefore, established the Maudsley BRC Prediction Modelling Group. This group provides a forum for researchers at King's College London’s Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and clinicians at South London and Maudsley NHS Foundation Trust, who are interested in prediction modelling applications for precision medicine. The group aims to increase communication, information exchange and collaboration between researchers.

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