Research
Our research is driven by the development of statistical methodology that is useful for working with imperfect longitudinal observational databases in biomedical applications. This includes research on causal inference, treatment effectiveness, missing data & loss to follow-up, epidemiology & biostatistics, quantitative methods in public health, causal fair machine learning and statistical modelling of infectious diseases and HIV.
Michael Schomaker is supported by the German Research Foundation's (DFG) Heisenberg Program. Katharina Ring and Han Bao work under the DFG project "Continuous Interventions in Epidemiology: from Theory to Practice". Christoph Wiederkehr works on missing data problems from a causal perspective as well as analyses in HIV research in collaboration with the University of Bern. All of us work on statistical aspects for the development of methods for causal inference with longitudinal continuous interventions, the implementation of statistical software and the application of the developed methods in pharmacoepidemiology, and public health in general.
Dr. Schomaker has contributed to many international research projects and collaborations, conducted analyses that directly informed WHO HIV Treatment Guidelines, and published many articles in peer-reviewed journals, as well as textbooks and teaching material.
Current lists of research articles: