Medical statistics and decision making

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Large amounts of data about our health are being collected in biobanks and clinical databases. We build the statistical methods to identify patterns in these data, to increase our understanding of the cause of disease, and of their prevention and management.

We focus on developing methods for statistical modelling in clinical and epidemiological studies and analysing large cohort data. We develop decision analysis techniques to support benefit-risk assessments of medicines and medical decision-making.

Our unit teaches statistical methods to undergraduate and postgraduate students through a range of basic and advanced courses, and we provide statistical support to other researchers through the Clinical Research Office and through short- and long-term collaborations with various clinical departments in the UMCG.

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Improved discovery of genetic variants in tumour samples

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Demographic inference from multiple whole genomes

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Deep Learning predicts 3D structure of genome

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How our research benefits to society

We focus on analysing large cohort and genomic data sets to further our understanding of disease and biology.

Teaching

We provide teaching on topics in machine learning and statistics, to equip students with the concepts and skills to correctly and efficiently set up studies and interpret results, enabling them to evidence-based improvement of standards of care.

Method development

We develop novel methodologies to add value to cohort databases such as Lifelines, by applying "unsupervised learning" techniques to these large databases. In addition we run studies in which we apply machine learning methods to functional genomic assays such as ATAC-seq and RNA-seq to improve our understanding of basic biology and cellular processes.