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Umeå University
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Staff photo Tetiana Gorbach

Tetiana Gorbach

Contact

E-mail
tetiana.gorbach@umu.se
Location
Samhällsvetarhuset, Biblioteksgränd 6 Umeå universitet, 901 87 Umeå
Phone
+46 90 786 72 01

Works at

Affiliation
Associate professor (on leave) at Umeå School of Business, Economics and Statistics (USBE) Units: Statistics
Affiliation
Associate professor at Umeå School of Business, Economics and Statistics (USBE) Units: Statistics

Project: "Selection bias in ageing studies: novel statistical methods for analyses of incomplete datasets" (Fortes international postdoc). 2 300 000 SEK. Project leader. 2022-2023.

Project: "Machine learning to study causality with big datasets: towards methods yielding valid statistical conclusions". Marianne och Marcus Wallenbergs Stilftelse. 6 000 000 SEK. Co-applicant. Project leader: Xavier de Luna. 2022-2027.

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DyNAMiC: A prospective longitudinal study of dopamine and brain connectomes: A new window into cognitive aging
Journal of Neuroscience Research, John Wiley & Sons 2022, Vol. 100, (6) : 1296-1320
Nordin, Kristin; Gorbach, Tetiana; Pedersen, Robin; et al.
When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging
NeuroImage, Academic Press 2021, Vol. 242
Pedersen, Robin; Geerligs, Linda; Andersson, Micael; et al.
Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains
Brain and Behavior, John Wiley & Sons 2021, Vol. 11, (2)
Ruotsalainen, Ilona; Glerean, Enrico; Karvanen, Juha; et al.
A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
Brain Connectivity, Mary Ann Liebert 2020, Vol. 10, (5) : 202-211
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE ε4 carriers
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, John Wiley & Sons 2020, Vol. 12, (1)
Gorbach, Tetiana; Pudas, Sara; Bartrés-Faz, David; et al.
Physical activity, aerobic fitness, and brain white matter: Their role for executive functions in adolescence
Developmental Cognitive Neuroscience, Elsevier 2020, Vol. 42
Ruotsalainen, Ilona; Gorbach, Tetiana; Perkola, Jaana; et al.
Methods for longitudinal brain imaging studies with dropout
Statistical studies, 54
Gorbach, Tetiana
Aerobic fitness, but not physical activity, is associated with grey matter volume in adolescents
Behavioural Brain Research, Elsevier 2019, Vol. 362 : 122-130
Ruotsalainen, Ilona; Renvall, Ville; Gorbach, Tetiana; et al.
Inference for partial correlation when data are missing not at random
Statistics and Probability Letters, Elsevier 2018, Vol. 141 : 82-89
Gorbach, Tetiana; de Luna, Xavier
Longitudinal association between hippocampus atrophy and episodic-memory decline
Neurobiology of Aging, Vol. 51 : 167-176
Gorbach, Tetiana; Pudas, Sara; Lundquist, Anders; et al.
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Gorbach, Tetiana; de Luna, Xavier; Waernbaum, Ingeborg; et al.
A Hierarchical Bayesian Mixture Modeling Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Bayesian mixture modeling for longitudinal fMRI connectivity studies with dropout
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Valid causal inference: model selection and sensitivity to unobserved confounding in high-dimensional settings
Moosavi, Niloofar; Gorbach, Tetiana; de Luna, Xavier
A note on sensitivity analysis for post-machine learning causal inference
Moosavi, Niloofar; Gorbach, Tetiana; de Luna, Xavier
Physical activity is positively related tolocal functional connectivity in adolescents’ brains
Ruotsalainen, Ilona; Glerean, Enrico; Karvanen, Juha; et al.
View publications in DiVA

Research groups

Group member
Stat4Reg

Research projects

1 June 2022 until 31 December 2026
Machine learning to study causality with big datasets: towards methods yielding valid statistical conclusions
Published: 03 Jun, 2021
Published: 31 May, 2021