Profile Susanne Strohmaier

Susanne Strohmaier

After graduating with a Dipl.-Ing. degree (equivalent to MSc) in Technical Mathematics from the Karl Franzens University in Innsbruck in 2010 and gaining some initial statistical experience at the Department for Medical Statistics, Informatics and Health Economics, Susanne Strohmaier moved to Lancaster (UK) to continue her studies in Medical Statistics (MSc, 2012).

Between 2012 and 2015 Susanne took her PhD in biostatistics taking part in the Marie Curie Initial Training Network – MEDIASRES, at the University of Oslo (Norway). There she worked on causal mediation analysis techniques in settings with time-to-event outcomes and repeatedly measured mediators. In 2016 she started her postdoctoral training in epidemiology at the Channing Division of Network Medicine and Harvard Medical School (Boston, USA) where she gained experience in analyzing large scale epidemiological studies, like the Nurses’ Health Study 2 (NHS2) and the Growing Up Today Study (GUTS). Particularly, she has been working on intergenerational analysis – linking the NHS2 and GUTS cohorts – to study associations between maternal lifestyle (diet quality) and occupational factors (rotating night shift work) during pregnancy and adverse outcomes in their offspring during childhood and adolescence, including obesity, depression/anxiety or alterations in stress marker responses.

In fall 2018 Susanne returned to Austria to work on her Marie Curie Individual Fellowship project - SCOUT (Supporting Causal Conclusions from Observational Survival Studies) at the Section for Clinical Biometrics (CeMSIIS, MUW), where they focus on evaluating different properties of multiplicative and additive hazard models in real-life scenarios and the comparison of methods for mediation analysis in the presence of rare (binary) mediators or outcomes in an ongoing project.

In October 2020 Susanne joined the Department of Epidemiology at the Medical University of Vienna, where she will continue her work on the cohort data as well as the development of epidemiological methods for causal inference in complex settings.