Research

In clinical trials and social studies it is to be expected that not all participants react equally to an intervention. Goal of this body of work is to estimate the effect of the intervention on an individual basis or for subgroups of the population. To achieve this well established methods (e.g. penalised regression) or modern methods such as regression trees can be utilized. Some of the questions arising from this are:

??? ?How can the predictions be validated?
??? ?Which method is the best to use in a particular setting?
??? ?How can groups of subjects on the basis of these predictions be defined?

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Traditional clinical trials are rigid in that any accumulating data is not used to improve the design of the trial. Moreover, the wealth of additional data, for example from other studies, is often not used. Using adaptive and Bayesian methods we work on methods to improve the efficiency of clinical trials. Our main focus is thereby dose-finding trials and studies, such as platform trials, that seek to answer multiple questions within one trial.

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One of the most promising applications of machine learning and AI is precision medicine. The expectation thereby is that these algorithms can yield accurate predictions about a patient‘s health on the basis of individual patient data (e.g. genetic make-up, epidemiological and environmental factors). Before a new drug can be used in patients a high level of evidence on its safety and effectiveness is required. In this research topic we investigate the question of how we can obtain a suitable level of evidence for algorithms that are sought to be used in healthcare. A particular question of interest is thereby how the dynamic nature of algorithms (i.e. the fact that they routinely get improved) can be accounte