Statistics for Stratified Medicine and Analysis of Complex Phenotypes

Many diseases and conditions, such as cancers, dementia and rheumatic disorders, are multi-factorial; exhibiting a range of biological manifestations that reflect the contribution of genetic and environmental factors. Even across individuals with rare diseases, which may result from single gene mutations, varying symptoms, health outcomes and treatment responses are seen. The complexity of diseases leads to many challenges ranging from understanding disease mechanisms and susceptibility to risk prediction and development and application of treatments. Stratified medicine, where "homogeneous" groups of people likely to respond similarly to treatment or have similar underlying disease mechanism or risk are sought based on biomarker information, is aimed at targeting therapies and optimal decision making for groups of patients who have shared biological characteristics. That genetic, molecular and imaging technologies have transformed biology has meant the need for those trained in analysing complex data, accounting for uncertainty and making evidence-based conclusions is paramount. Our programme will address statistical issues arising from using complex biological and clinical data for further understanding of diseases and to develop models aimed at risk prediction, stratified prevention, projection of disease course, treatment response, safety and the likelihood of adverse events and the tailoring of therapeutics to individuals.