Tibor Schuster

Dr Schuster accomplished his early academic and professional education at the Ludwig Maximilian University (LMU) of Munich and the Institute for Medical Statistics and Epidemiology at the Technical University of Munich (TUM). He obtained his doctorate in Biostatistics from the Faculty of Mathematics, Informatics and Statistics at the LMU. Subsequently, he received a post-doctoral award from the Canadian Network of Observational Drug Effect Studies (CNODES) and carried out a post-doctoral fellowship in pharmacoepidemiology at the Department of Epidemiology, Biostatistics and Occupational Health, McGill University and the Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research in Montreal. He continued with a research fellowship at the Murdoch Childrens Research Institute in Melbourne where he was acting Director of Biostatistics at the newly established Melbourne Children’s Trial Centre in 2015. In August 2016, Dr Schuster started a tenure-track faculty position as Assistant Professor at the Department of Family Medicine. He is holder of a Tier II Canada Research Chair in Biostatistical Methods for Primary Care Research. In 2019, he's been the acting Director of the Methods Development Component of the Quebec SPOR-SUPPORT (Strategy for Patient-Oriented Research Support for People and Patient-Oriented Research and Trials) Unit and since July 2019, the Graduate Program Director for the Ph.D. program and Postdoctoral Fellows at the Department of Family Medicine. Dr Schuster taught biostatistical methods at renowned institutions in Germany, Canada and Australia. He acted and is acting as supervisor and mentor for graduate and doctoral students in the fields of biostatistics, epidemiology and bio-medical research. Research Interests: Dr Schuster’s main methodological interests are in the development and application of causal inference methods for the design and analysis of cluster randomized controlled trials and observational research studies based on administrative or electronic medical / health record data. Randomised controlled trials are considered to be the gold standard for inference on intervention effects in bio-medical research and health sciences. If rigorously conducted, such trials yield unbiased and consistent estimates of average intervention effects in relevant target populations. However, systematic patient drop-out and missing data issues occur frequently and can lead to substantial bias in effect estimation if not considered appropriately. Furthermore, treatment cross-over, non-adherence or non-compliance as well as subsequent (often event-driven) changes of individual treatment protocols require sophisticated analysis strategies to enable estimation of meaningful population-level effects. Recent methodological developments, in particular so called causal inference approaches, provide promising solutions to these problems. However, for an effective implementation, consideration of relevant data to be collected is compulsory at the design stage, which is a shortcoming of many past and currently ongoing research studies. Furthermore, the immense amount of emerging data due to modern electronic sources requires computational and algorithmic intelligence that goes beyond conventional statistical modelling. Dr Schuster therefore encourages the incorporation and application of modern Machine Learning techniques in conjunction with fundamental principles of Causal Inference. His specific methodological interests are in: - Design and analysis of Cluster Crossover Trials, in particular Stepped Wedge Designs - Causal Inference methods such as Marginal Structural Models and Targeted Learning, - Theory and applications of Personalized Medicine and Dynamic Treatment Regimens such as Sequential, Multiple Assignment, Randomized Trial (SMART) designs - Bayesian adaptive and sequential study designs, in particular Internal Pilot Studies and so called Platform Trials - Confounder selection and adjustment in high dimensional covariate settings - Modern methods for Statistical and Machine Learning and Data Visualization