Using advanced algorithms, wearables, and clinical, molecular, and lifestyle data, we identify people at increased risk of disease and develop individually adapted preventive interventions.
Our goal is to establish as precisely as possible which disease risks can be influenced and which measures can reduce them effectively. A central scientific challenge is to determine the true effect of a preventive intervention: If a predicted disease does not occur, it is initially unclear whether it was prevented by the intervention or would not have developed anyway. We therefore develop statistical and causal inference methods that help distinguish prediction, the natural course of disease, and the effects of prevention as reliably as possible. At the same time, it is important to us to communicate that prevention can reduce risk, but cannot prevent every case of cancer. The development of disease depends on many biological, environmental, and even random factors.
In addition, we operate the Dresden site of the National Cancer Prevention Center. There, we bring together research, medical care, and prevention services to develop, scientifically evaluate, and translate innovative approaches to cancer prevention and early detection into practice.
