Title: Statistical modelling for predicting patient response to depression therapy
Postdoc Fellow: Brice Ozenne, NRU and Section of Biostatistics, University of Copenhagen
Every year, 1 out of 15 Europeans suffer from major depression (MDD) and MDD is the third cause of disability-adjusted life years. Today, the available treatments are clearly insufficient; only about 50% of MDD patients respond to drug intervention. We here posit that identification of biomarkers that can predict treatment response is needed to adapt a personalized medicine approach. Most likely this will involve not a single outcome but a combination of multimodal brain imaging outcomes, psychological, genetic, and environmental data. The complexity of such data requires a complex statistical model that currently does not exist.
Thus, the aim of this project is to develop a new flexible statistical method that can take into account heterogeneous types of data. More specifically, a flexible Latent Variable Model (LVM) will be developed that can deal with high dimensional measurements (e.g. images) and non-Gaussian variables (e.g. categorical scores). This flexible LVM will be applied on human data to derive a predictor of patient response following depression therapy. The data are currently being acquired in NP1 and includes a cohort of MDD patients treated with a selective serotonin reuptake inhibitor (SSRI), followed in a longitudinal design. The project will also make use of existing data from the Cimbi database, which is an established unique database including, e.g., functional Magnetic Resonance Imaging (fMRI), high resolution Positron Emission Tomography (PET), and neuropsychological test outcomes.
This project uniquely combines advanced statistical modelling of rich data sets with the aim to establish individualized depression therapy. Moreover, it forms a foundation for a more general approach to integrate brain neurobiology in terms of imaging outcomes with other patient-specific data.