Biostatistics; Epidemiologic Methods; Epidemiology
- Prognosis research
Research Area: Prognosis research in medicine aims at understanding and improving future outcomes of individuals. We work on aspects of developing, translating and applying statistical methodology in prognosis research. Co-leaders: Daniela Dunkler and Georg Heinze
Techniques, methods & infrastructure
As large scale cohort studies result in complex data structures involving longitudinal observations and time-to-event outcomes and aim at answering causal research questions, most of my methodological research so far has focused on causal inference methods that allow to deal with these data set-ups. In particular, I was involved in developing different methods to estimate direct and indirect effects in settings with a repeatedly measured mediator and a time-to-event outcome. Other ongoing work in the context of mediation analysis focuses on the handling of rare (binary) mediators or outcomes.
A general interest for time-to-event analysis techniques as well as applications in clinical epidemiology has inspired research on the comparison between additive and multiplicative hazard models in practice. Specifically, we are comparing different aspects of Cox's proportional hazards model and Aalen's additive hazard model in context of causal modeling on the one hand, but also predictive and descriptive modelling on the other hand.
- SCOUT - Supporting Causal Conclusions from Observational Survival Studies (2018)
Source of Funding: EU, H2020-MSCA-IF-2017
- Strohmaier, S. et al., 2019. Night shift work before and during pregnancy in relation to depression and anxiety in adolescent and young adult offspring. European Journal of Epidemiology, 34(7), pp.625,
- Aalen, O.O. et al., 2019. Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model. Biometrical Journal. Available at: http://dx.doi.org/10.1002/bimj.201800263.
- Strohmaier, S. et al., 2019. Maternal rotating night shift work before pregnancy and offspring stress markers. Physiology & Behavior, 207, pp.185–193. Available at: http://dx.doi.org/10.1016/j.physbeh.2019.05.007.
- Strohmaier, S. et al., 2015. Dynamic path analysis - a useful tool to investigate mediation processes in clinical survival trials. Statistics in Medicine, 34(29), pp.3866–3887. Available at: http://dx.doi.org/10.1002/sim.6598.
- Aalen, O.O. et al., 2017. Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models. Scandinavian Journal of Statistics, 45(1), pp.62–86. Available at: http://dx.doi.org/10.1111/sjos.12286.