David Svensson (AstraZeneca), Ilya Lipkovich (Eli Lilly), Björn Bornkamp (Norvatis), Kostas Sechidis (Novartis), Paolo Eusebi (UCB Pharma)
In this webinar we look at some recent advances in statistical methods for identifying treatment effect heterogeneity in clinical trials. This ranges from identifying baseline biomarkers likely to influence the treatment effect (ranking) to provide novel biomarker 'signatures' (subgroups) with associated estimated enhanced effect (Individual Treatment Effects). Some practical issues ranges from overfitting risks, biases, and confounding of prognostic and predictive effects. Modern methods aim to overcome such potential difficulties while remaining flexible, and offer a structured approach to the problem (aiming to avoid the notorious 'data dredging'). The novel techniques are often tree based and/or penalized regression, i.e., with a machine learning flavour. Sometimes the aim of the analysis is to predict the individual optimal treatment allocation given baseline biomarker data (Individual Treatment Rules). Efficient Visualization of relationships in the data is also of importance in the practical applications. The talks will highlight and discuss such aspects and will also reflect typical aspects discussed within the EFSPI/PSI Subgroup Special Interest Group. (While this event is not intended as a formal course, it will still serve as an introduction and overview to the area, as well as covering some more technically challenging material for the more experienced participant).