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Statistical Modeling

Linear Mixed Effects Models
Linear mixed effects (LME) models are optimally suited for the analysis of longitudinal data. They can deal well with missing data, drop-outs or different time intervals. In a collaboration with Dr. Sabuncu we have introduced LME models to neuroimaging applications [3] and extended it for the analysis of spatio-temporal signals on the cortical surface [2]. Proximity regions on the cortex demonstrate similar correlation patterns, a fact that can be exploited to increase statistical power as well as improve run-time of the LME models. We furthermore extended the model to time-to-event analysis in [1], by sampling missing data points from the LME fit, allowing inclusion of time varying covariates that were not observed in all subjects at all event times.
Non-linear Modeling of Disease Trajectories
Another topic of interest is the non-linear modeling of disease trajectories. While atrophy rates are often modeled with a linear approach, there is evidence that rates increase around the time of conversion and potentially reach a floor effect at advanced progression [4,5]. Due to this non-linear behavior, atrophy rates may help predict time-to-disease-onset or allow selection of candidates into promising disease-modifying therapies at early pre-symptomatic stages.
Selected Publications (full list):
Martin Reuter - MIT - Cambridge, MA, USA - EMail:
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